Construction and infrastructure are designed to meet societal needs over extended periods. However, their limited operational flexibility often results in suboptimal service delivery during changing demand scenarios. Despite recent technological advancements sparking interest in responsive systems that enhance the operational efficiency of infrastructure in the face of demand fluctuations, a comprehensive systematic analysis identifying clear trends, technological maturity and implementation barriers remains lacking. This study addresses the current gap in understanding by providing a comprehensive review of responsive systems in the built environment.
The study employs a systematic methodology for literature selection, guided by six criteria and analyzes the selected works across three levels. This approach ensures thorough coverage and a structured understanding of the research landscape related to responsive systems in the built environment.
The analysis reveals significant growth in research on responsive systems for the built environment, particularly since 2016. Key applications include responsive HVAC, partitions, alert systems, traffic control and energy management, driven by demand for safety, energy efficiency and operational optimization. Residential and commercial buildings, transport hubs and healthcare facilities dominate as testbeds, benefiting from the availability of mature sensing technologies for HVAC and lighting solutions. These trends underscore the field’s rapid evolution and application potential.
This study offers a systematic review of responsive systems in the built environment, addressing the lack of comprehensive analysis in the field. It provides valuable insights into emerging technologies and their potential to enhance infrastructure flexibility and operational efficiency, paving the way for innovative solutions in dynamic urban systems.
1. Introduction
Construction and infrastructure are constructed with the expectation of operating over long life spans, often spanning decades, and under variable conditions. Under such operating conditions, assets designed to deliver constant performance may struggle to maintain satisfactory service levels over time. This challenge arises from the inherent trade-off between designing for standard operational demands, resulting in undersized infrastructure and inefficiency, or designing for maximum demand, leading to oversized and ineffective infrastructure for the majority of its operational life.
To address these limitations and enhance the effectiveness and efficiency of the built environment, infrastructure should be engineered for responsive operations rather than static ones. Responsive infrastructures are capable of dynamically adjusting their performance in real-time to accommodate changing environmental conditions. For example, train stations equipped with dynamic gates that allow passengers inflow subject to the available space inside the station (Martani et al., 2017; Lai et al., 2022) or dynamic roadways that automatically allocate space to different transport modes according to evolving demand (Broennimann, 2023).
Responsive infrastructures feature systems comprising three key components: (1) sensing technologies to monitor environmental conditions affecting infrastructure services (such as temperature, lighting, occupancy, or vehicle speed on highways), (2) actuators or response mechanisms that modify infrastructure characteristics or performance based on sensed conditions (like windows that automatically adjust based on indoor air quality and weather conditions), and (3) an interface linking sensors and actuators through a triggering logic. These components work together to collect, process, and utilize vast amounts of data to optimize infrastructure performance.
Recent developments in sensing technology (Kumar et al., 2015), Internet of Things (IoT) (Tang et al., 2019), machine-to-machine (M2M) communications, artificial intelligence (AI) (Seyedzadeh et al., 2018; Ullah et al., 2020), Digital Twin (Ghansah, 2024) and robotics (Pan et al., 2018) have laid the groundwork for innovative applications of responsive systems. Additionally, Aghimien et al. (2020) highlighted that challenges related to governance, economy, society, technology, environment, and legal frameworks significantly affect smart city development in developing countries, emphasizing the necessity of a holistic approach when implementing responsive technologies. As highlighted by Carlucci (2021), one of the critical deficiencies of traditional buildings lies in their inability to adapt dynamically to changing environmental conditions, significantly limiting their energy efficiency and indoor comfort performance. Carlucci's comprehensive review further emphasized the rapid advancement and diverse classifications of smart and responsive building technologies, suggesting the need for clearer definitions and standardized nomenclature to streamline their integration into high-performance buildings. Despite the construction industry’s known reluctance to adopt new technologies (Setaki and van Timmeren, 2022), these advanced systems have begun to find application across various types of infrastructure.
Notwithstanding numerous recent endeavors to employ sensing and responsive systems for optimizing the operation of the built environment, a comprehensive and thorough analysis of these efforts is still lacking. Suo et al. (2024) recently took an initial step toward addressing this gap by providing valuable insights into the current advancement of responsive infrastructure. While commendable for initially mapping the state of research in the field, this work was limited in two ways. First, the classification of sensing technologies overlooked those used in healthcare environments. Second, responsive systems were broadly classified into four general categories—information, alert response, automatic response, and alert and automatic response—without exploring specific types of responsive solutions. This general classification hindered the explicit identification of the precise types of automatic responses (e.g. HVAC control, lighting adjustments, partition management) and their application.
To address these limitations, the present paper introduces a systematic literature review, that: (1) provides a detailed classification of sensing technologies, expanding from 9 to 15 categories to include those previously overlooked, such as air quality sensors and biosensors used in healthcare environments; (2) classifies responsive solutions into 12 specific types of automatic responses, allowing for explicit identification of the precise types of automatic responses and their applications; (3) performs a three-level analysis to identify frequencies, trends, and gaps in the development of responsive infrastructure and (4) evaluates the full spectrum of responsive technologies—including sensing technologies, actuating systems, and IoT devices—used within various built environments, thereby addressing the fragmented nature of existing categorizations and providing a holistic analytical framework.
By providing a more detailed and comprehensive analysis, this work aims to enhance the understanding of how sensing technologies, responsive systems, and triggering logic are utilized to improve the effectiveness and efficiency of the built environment, underlining the current capabilities, gaps, and future prospects of responsive systems for the built environment.
2. Method of the analysis
In this section, the systematic literature review of the state of the art in the use of responsive systems for the built environment has been developed with an explicit and detailed classification of both the sensing and the responding systems. The methodology used to ensure a comprehensive and systematic literature develops along three-steps: selection, classification, and analysis of relevant scientific publications.
2.1 Selection criteria
The relevant papers were selected according to four criteria: works (1) published within the last ten years (2013–2023), (2) focused on sensing and responding systems, (3) applied to the operation and management of buildings, infrastructure or urban environments, (4) published in English language, peer-reviewed journals and conferences. The selection of publications according to criteria 1 and 2 was ensured through the use of appropriate keywords in the search, including “responsive systems”, “smart systems”, “adaptive technologies”, “built environment”, “buildings”, “urban infrastructure”, “sensor”, “IoT” and “actuator”. The selection was conducted using major scientific publication databases, including Google Scholar, Scopus, and ScienceDirect. The initial keyword-based search yielded a total of 275 publications. After title and abstract screening, 142 articles were retained for full-text review. Finally, 99 studies met all inclusion criteria and were selected for detailed analysis. The inclusion of co-term analysis allowed to move beyond keyword frequency and explore the relationships between key concepts in the field.
2.2 Classification criteria
The selected articles were then classified based on six criteria to capture the evolution of studies over time, technologies and application: (1) publication year, (2) application sector, (3) sensing technology used, (4) response type, (5) triggering logic, and (6) technology maturity level. To overcome the limitations as from Suo et al. (2024), in this work responsive solutions were classified in 12 specific types of automatic responses, while the classification of sensing technology moved from 9 to 15, to include also these previously overlooked, e.g. sensors of air quality and biosensors particularly used for responsive healthcare environments. The classifications for each criterion have been derived from the papers reviewed. The specific breakdown and classification of each criterion are outlined in Table 1.
Criteria to classify the selected publications
| Criteria | Classified in … |
|---|---|
| Year | Accordance to the year of publication, to identify any patterns or trends over time |
| Sector of application | (1) building management; (2) transportation infrastructure; (3) healthcare facility; (4) dams and irrigation, and (5) others like energy harvesting or hazard protection facility |
| Sensing technology used | (1) Air quality: Sensors detecting pollutants of other particles concentration, e.g. including CO2 content, flames, gas, NO2, O3, particles, smoke (2) Biosensor: Sensors measuring biological/chemical reactions, e.g. biological activities, heart rate and SpO2, which measuring blood oxygen saturation (3) Distance: Includes ultrasonic (diffuse proximity, retro-reflective, through-beam) and optical sensors (based on wavelength, frequency, polarization of light) (4) Electrical: Sensors that detect or measure electrical changes (5) Humidity: Sensors measuring the amount of water vapor in the air (6) Imaging: like CCTV(Closed-Circuit Television) for surveillance, RGB (Red, Green, Blue) sensors and RGBD (Red, Green, Blue, Depth) sensors (7) Light: Sensors detecting light or changes in lighting conditions (8) Moisture: measuring the level of water content in environment or material (9) Motion: Sensors detecting movement in an area (10) Occupancy: recognizing access or presence of bodies in a defined space, e.g. using infrared radiation, microwaves, acoustics, ultrasonic and Wi-Fi signals (11) Power: Sensors measuring the rate of energy usage or generation (12) Pressure: Sensors detecting the force exerted on a surface (13) Temperature: including thermometers in climate and medical system (14) Other [NEI]: e.g. rain and AVL (automated vehicle location) (15) Not specified [NA] |
| Type of response | (1) Alert response: only an alert signal is triggered in critical situations (e.g. structural monitoring of beam and column) (2) control of electricity: regulating power flow, intensity, voltage and energy use (3) control of elevator: referring to the management and regulation of elevators, escalators, and other vertical transit systems (4) control of emergency system: fire alarms, fire suppression systems, and evacuation plans (5) control of energy: monitoring the use of various energy-consuming systems and equipment, as well as implementing energy-saving strategies and technologies (6) control of HVAC (Heating, Ventilation, and Air Conditioning): regulation and management of temperature, humidity, and air flow in a building or enclosed space to maintain a comfortable indoor environment and improve air quality (7) control of lighting: optimizing lighting for comfort, safety and energy efficiency (8) control of partition: managing movable partitions or dividers within spaces (9) control of traffic: referring to the supervision of the movement of people, goods, or vehicles to ensure efficiency and safety (10) control of valve: managing the flow of liquids, gases, or other substances (11) information: no change in the infrastructure is triggered and the only reaction planned is a data recording on critical situations (12) information and alert: both an alert signal and information are executed |
| Triggering logic used | (1) no triggering: systems work without the need for triggering (2) explicit triggering: triggering logic is clearly reported (3) implicit triggering: triggering logic exists but is not explained in the paper |
| Maturity of technology | Accordance to the Technology Readiness Level (TRL) TRL1, Basic principles observed and reported TRL2, Technology concept and/or application formulated TRL3, Analytical and experimental critical function and/or proof of concept TRL4, Component and/or breadboard validation in a laboratory environment TRL5, Component and/or breadboard validation in a relevant environment TRL6, System/subsystem prototype demonstration in relevant environment TRL7, System/Subsystem model/prototype in operational environment TRL8, Actual system completed and qualified through test and demonstration TRL9, Actual system proven through successful mission operations |
2.3 Analysis levels
The publications selected and classified according to the above detailed criteria have then been analyzed on three levels to identify frequencies, trends, and gaps in the development of responsive infrastructure:
Single Interpolation – In level 1 analysis, the frequency distribution of each classification criteria has been examined, including technology readiness levels (TRL), sensing technologies, infrastructure sectors, trigger types, response types, and publication year. Each classification characteristic was grouped into clearly defined categories, as detailed in Table 1, and frequency distributions were calculated accordingly—for instance, determining the number of publications categorized at TRL levels 7, 8, or 9.
Dual Interpolation – In level 2 analysis, relationships between two classification characteristics were investigated through a dual-variable cross-analysis. Specifically, frequency distributions were evaluated by cross-referencing two criteria simultaneously. For example, the analysis identified the number of publications utilizing occupancy (motion) sensors specifically for the control of HVAC systems. This analysis enabled clear identification of correlations between different research dimensions.
Triple Interpolation – Joint analysis involving three classifications simultaneously (e.g. sensors, infrastructure sectors, and TRL). This step involved simultaneously examining frequency distributions across three classification characteristics to reveal complex interactions and nuanced research trends. For example, this analysis identified how many studies published in 2021 addressed responsive systems specifically designed for traffic control in transportation infrastructures. This advanced analytical perspective provided deeper insights into the intersections of technological maturity, application contexts, and temporal research trends, facilitating the precise detection of existing research gaps and emerging development patterns.
3. Findings and discussion
The 99 publications selected for the analysis are reported in Table 2 along with their classification characteristics. The results of the 3 levels analysis are further shown in Figures 1–5 and discussed in the following text.
Classification of the selected research on responsive infrastructure
| Reference | Infrastructure | Sensing technology | Response | Trigger logic | TRL |
|---|---|---|---|---|---|
| Coelingh et al. (2006) | Transportation | Distance Imaging | Control of Energy | Explicit | 7 |
| Urbikain and Sala (2009) | Buildings | Temperature | Control of Energy | Explicit | 5 |
| Lu et al. (2010) | Buildings | Motion | Control of HVAC | Explicit | 7 |
| Mathur et al. (2010) | Transportation | Distance | Control of Traffic | Explicit | 7 |
| Agarwal et al. (2010) | Buildings | Motion | Control of HVAC | Explicit | 8 |
| Park et al. (2010) | Hospital | Biosensor Pressure Temperature | Information and Alert | Explicit | 8 |
| Chen et al. (2010) | Hospital | Biosensor Motion Temperature | Information and Alert | Explicit | 8 |
| Yao et al. (2011) | Hospital | Motion | Alert Response | Explicit | 2 |
| Sharma et al. (2011) | Transportation | Motion | Information and Alert | Explicit | 7 |
| Dilmaghani et al. (2011) | Hospital | Biosensor | Information and Alert | Explicit | 6 |
| Ni et al. (2012) | Buildings | Imaging | Alert Response | Explicit | 7 |
| Weng and Agarwal (2012) | Buildings | Occupancy | Control of HVAC | Explicit | 2 |
| Visvanathan et al. (2012) | Hospital | Motion | Information and Alert | Explicit | 7 |
| Balaji et al. (2013) | Buildings | Occupancy | Control of HVAC | Explicit | 9 |
| Saad et al. (2013) | Transportation | Light Motion | Control of Traffic | Explicit | 6 |
| Torfs et al. (2013) | Buildings | Humidity Motion | Control of HVAC Control of Lighting Control of Partitions | Explicit | 8 |
| Peng and Qian (2014) | Buildings | Power | Control of Electricity | Explicit | 8 |
| Öörni et al. (2015) | Buildings | Power | Control of Electricity | Explicit | 7 |
| Gruber et al. (2014) | Buildings | Humidity Motion | Control of HVAC Control of Lighting Control of Emergency Systems | Explicit | 6 |
| Gonnot et al. (2014) | Hospital | Biosensor Pressure Temperature | Information and Alert | Explicit | 7 |
| Marzouk and Abdelaty (2014) | Buildings | Temperature | Control of HVAC | Explicit | 7 |
| Chiuchisan et al. (2014) | Hospital | Distance | Information and Alert | Explicit | 8 |
| Yang et al. (2014) | Buildings | Motion | Control of HVAC | Explicit | 7 |
| Orrie et al. (2015) | Buildings | Imaging | Alert Response | Explicit | 7 |
| Suryadevara et al. (2014) | Transportation | NEI | Control of Emergency Systems | Implicit | 9 |
| Li and Mi (2015) | Transportation | NA | Control of Electricity | Explicit | 9 |
| Madli et al. (2015) | Transportation | Distance | Alert Response | Explicit | 7 |
| Ajith et al. (2015) | Hospital | Biosensor | Information and Alert | Explicit | 8 |
| Ajami and Khaleghi (2015) | Hospital | Humidity Motion Pressure Pressure | Information and Alert | Explicit | 8 |
| Dunlop et al. (2016) | Transportation | Motion | Alert Response | Explicit | 4 |
| Abid et al. (2016) | Buildings | Temperature | Control of HVAC | Explicit | 6 |
| Sharma and Bedi (2016) | Others | NA | Alert Response | No Trigger | 9 |
| Lee et al. (2016) | Transportation | Imaging Motion | Information | No Trigger | 5 |
| Kanase and Gaikwad (2016) | Transportation | Other | Control of Traffic | Explicit | 6 |
| Nellore and Hancke (2016) | Buildings | Light | Alert Response | Explicit | 2 |
| Lakshminarasimhan (2016) | Transportation | Motion | Control of Traffic Control of Energy | Explicit | 9 |
| Favoino et al. (2016) | Buildings | Temperature | Control of Partitions | Explicit | 6 |
| Biloria (2016) | Buildings | Distance Motion | Control of Partitions | Implicit | 9 |
| Hoy (2016) | Buildings | Occupancy | Control of HVAC | Explicit | 5 |
| Kim et al. (2016) | Buildings | Air Quality Temperature | Control of HVAC | Explicit | 7 |
| Gultom et al. (2017) | Dams and Irrigation | Moisture | Control of Valve | Explicit | 6 |
| Vishwas and Ullas (2017) | Buildings | Motion | Control of Lighting | Explicit | 7 |
| Hu et al. (2017) | Hospital | Biosensor | Alert Response | Explicit | 4 |
| Yamamoto et al. (2017) | Hospital | Biosensor | Alert Response | Explicit | 5 |
| Dey et al. (2017) | Hospital | Biosensor | Information and Alert | Explicit | 8 |
| Dementyev et al. (2017) | Hospital | Pressure | Information and Alert | Explicit | 3 |
| Torres et al. (2017) | Hospital | Motion | Information and Alert | Explicit | 6 |
| Gargoum et al. (2017) | Transportation | Distance | Information and Alert | Explicit | 6 |
| Lakkis and Elshakankiri (2017) | Hospital | Temperature | Information and Alert | Explicit | 8 |
| Aviv (2018) | Transportation | Power | Alert Response | No Trigger | 9 |
| De Belie et al. (2018) | Others | NA | Alert Response | No Trigger | 9 |
| Khan et al. (2018) | Buildings | NEI | Control of Partitions | Implicit | 6 |
| Yang et al. (2018) | Transportation | Distance | Alert Response | Explicit | 6 |
| Perilla et al. (2018) | Buildings | Temperature | Control of Lighting | Explicit | 7 |
| Al-Yemni et al. (2018) | Buildings | Temperature | Control of HVAC | Explicit | 8 |
| Geman et al. (2018) | Hospital | Temperature | Information and Alert | Explicit | 8 |
| Pereira and Nunes (2018) | Hospital | Motion | Information and Alert | Explicit | 6 |
| Mary Reena et al. (2018) | Buildings | Occupancy | Control of HVAC | Explicit | 8 |
| Asthana et al. (2019) | Transportation | Temperature | Control of Energy | Explicit | 1 |
| Alghamdi and Almawgani (2019) | Buildings | Motion | Control of Partitions | Explicit | 7 |
| Ren et al. (2019) | Others | Moisture | Control of Energy | Explicit | 4 |
| Hosseini et al. (2019) | Buildings | Light | Control of Partitions | Explicit | 3 |
| Wang et al. (2019) | Buildings | NA | Control of Partitions | No Trigger | 7 |
| Toh et al. (2019) | Transportation | Occupancy | Control of Traffic | Explicit | 5 |
| Daniel et al. (2019) | Buildings | Imaging | Alert Response | Explicit | 7 |
| Angelov et al. (2019) | Hospital | Biosensor | Alert Response | Explicit | 8 |
| Sony et al. (2019) | Buildings | Motion | Alert Response | Explicit | 3 |
| Cho (2019) | Hospital | Distance | Information and Alert | Explicit | 8 |
| Ji et al. (2019) | Buildings | Temperature | Control of HVAC | Explicit | 7 |
| Liu et al. (2019) | Buildings | Temperature | Control of HVAC | Explicit | 8 |
| Lu et al. (2020) | Transportation | Light | Control of Lighting | Explicit | 9 |
| Niranjan and Rakesh (2020) | Buildings | Other | Control of Lighting | Explicit | 7 |
| Angdresey et al. (2020) | Others | Temperature | Information | Explicit | 6 |
| Liu et al. (2020) | Others | Temperature | Alert Response | Explicit | 6 |
| Koroniotis et al. (2020) | Transportation | Motion | Information and Alert | Explicit | 7 |
| Samha et al. (2020) | Buildings | Occupancy | Control of HVAC | Explicit | 7 |
| Sharma (2020) | Hospital | Biosensor | Information and Alert | Explicit | 7 |
| Abdulkareem et al. (2021) | Buildings | Temperature | Control of Partitions | Explicit | 6 |
| Jiang et al. (2021) | Buildings | NA | Control of Partitions | No Trigger | 7 |
| Liu et al. (2021) | Buildings | Air Quality | Control of HVAC | Explicit | 8 |
| Ilham et al. (2021) | Buildings | Air Quality | Control of HVAC | Explicit | 7 |
| Saleem and Hosoda (2021) | Transportation | Temperature | Control of Lighting | Explicit | 9 |
| Sai Sri Vastava et al. (2021) | Transportation | Motion | Control of Traffic | Explicit | 9 |
| Madhuri et al. (2021) | Transportation | Motion | Control of Traffic | Explicit | 6 |
| Laxman and Jain (2021) | Buildings | Pressure | Control of Elevator | Explicit | 6 |
| Aditya et al. (2021) | Dams and Irrigation | Distance | Control of Valve | Explicit | 4 |
| Visvanathan et al. (2022) | Hospital | Motion | Alert Response | Explicit | 3 |
| Ariansyah et al. (2021) | Buildings | Imaging | Control of Partitions | Explicit | 6 |
| Said et al. (2021) | Hospital | NA | Information and Alert | Explicit | 6 |
| Brintha et al. (2022) | Others | Temperature | Information | No Trigger | 7 |
| Gnanavel et al. (2022) | Transportation | Motion | Control of Partitions | Explicit | 9 |
| He et al. (2022) | Buildings | Temperature | Control of HVAC | Explicit | 6 |
| Nicholson et al. (2022) | Dams and Irrigation | Moisture | Control of Valve | Explicit | 9 |
| Krishnan et al. (2022) | Transportation | Motion | Alert Response | Explicit | 8 |
| Christakis et al. (2022) | Buildings | Air Quality | Information | No Trigger | 6 |
| Soudian and Berardi, (2022) | Buildings | Temperature | Control of Valve | Explicit | 7 |
| Lu et al. (2021) | Hospital | Biosensor | Alert Response | Explicit | 5 |
| Mursidah et al. (2022) | Buildings | Light | Control of Lighting | Explicit | 7 |
| Taheri et al. (2022) | Buildings | Motion | Control of HVAC | Explicit | 7 |
3.1 Level 1 analysis – Single Interpolation
The analysis of the six classification characteristics in Figure 1 reveals several key trends and insights regarding the application of responsive systems in the built environment. The significant increase in publications between 2018 and 2022 suggests a growing interest and momentum in developing these systems, reflecting advancements in both technology and practical applications. This trend aligns with the broader focus on creating adaptive and intelligent environments to enhance comfort, efficiency, and safety. The high proportion of explicitly provided triggering conditions (88 out of 99 cases) highlights the emphasis on clearly defining the parameters that activate these systems, which is crucial for reliable functionality. It suggests a matured understanding of responsive systems and a deliberate approach to optimizing their operational efficiency. In terms of sectoral distribution, the dominance of building construction (44%), transportation (23%), and hospitals (23%) indicates that these sectors are key focal areas. Buildings and transportation systems, being the most dynamic and interactive with human activity, naturally require responsive technologies to optimize spatial configurations and environmental conditions. Hospitals, where safety and precision are critical, benefit significantly from automated monitoring and responsive controls. The technology readiness levels (TRLs) of these applications further reinforce the focus on practical implementation. With 81% of studies at TRL 6–9 and a notable 31% reaching TRL 8 or 9, it's clear that research is progressing beyond theoretical exploration and into real-world prototyping and deployment. This emphasis on higher TRL levels indicates confidence in the technology’s viability and reliability in real-world scenarios. Regarding sensing technologies, motion sensors (39%) and temperature sensors (27%) are the most widely utilized, reflecting a predominant focus on monitoring spatial occupancy and thermal conditions. This is indicative of the drive to create responsive environments that adapt to the number and behavior of occupants, ensuring comfort, energy efficiency, and safety. The use of biosensors and distance sensors, albeit less prevalent (14% each), suggests specialized applications where human physiological data or spatial measurements are necessary, such as in healthcare or security contexts. The types of responses also highlight key priorities in responsive systems. In building environments, Ariansyah et al. (2021) implemented advanced imaging sensors (TRL 6) to enable dynamic control of partitions, optimizing spatial flexibility and privacy in real-time according to occupant needs. Similarly, Taheri et al. (2022) successfully utilized motion sensors (TRL 7) for adaptive HVAC control systems, effectively managing thermal comfort and energy consumption by dynamically responding to occupant movement and activity patterns. The predominant use of HVAC control (24%) indicates a primary focus on maintaining indoor air quality and comfort through automated temperature and ventilation adjustments. This is followed closely by passive responses, such as information and alert systems (20%) and alert-only responses (19%), which aim to provide critical information without direct environmental changes. The control of partitions (12%) as a dynamic response suggests an interest in flexible space management, especially in contexts like co-working environments or healthcare facilities where spatial adaptability is crucial. Overall, the findings illustrate a clear shift towards practical deployment and adaptive management of space, energy, and safety through responsive systems. The distribution of technologies and responses underscores the diverse applications and the need to cater to varying environmental and user-specific conditions. The emphasis on higher TRL levels and well-defined triggering mechanisms points to a maturing field that is increasingly focused on delivering tangible benefits and reliable outcomes.
The figure shows six pie charts arranged in three rows of two. The first chart on the top left is titled “Types of Infrastructure.” The data from the chart in the clockwise sense are as follows: Buildings: 44. Transportation: 23. Hospital: 23. Others: 6. Dams and Irrigation: 3. The second chart on the top right is titled “T R L Level.” The data in the clockwise sense are as follows: T R L 1: 1. T R L 2: 3. T R L 3: 4. T R L 4: 4. T R L 5: 6. T R L 6: 21. T R L 7: 29. T R L 8: 18. T R L 9: 13. The third chart in the middle left is titled “Publishing Year.” The data in the clockwise sense are as follows: 2006: 1. 2009: 1. 2010: 5. 2011: 3. 2012: 3. 2013: 3. 2014: 6. 2015: 6. 2016: 10. 2017: 11. 2018: 9. 2019: 13. 2020: 7. 2021: 11. 2022: 10. The fourth chart in the middle right is titled “Types of Triggering Logic.” The data in the clockwise sense are as follows: No Trigger: 8. Explicit Triggering: 88. Implicit Triggering: 3. The fifth chart on the bottom left is titled “Type of Sensing Technologies.” The data in the clockwise sense are as follows: Motion: 39. Temperature: 27. Distance: 14. Biosensor: 14. Light: 9. Air Quality: 9. Pressure: 9. Imaging: 8. N A: 6. Occupancy: 6. Humidity: 6. N E I: 4. Moisture: 4. Power: 3. Electrical: 1. The sixth chart on the bottom right is titled “Type of Response.” The data in the clockwise sense are as follows: Control of H V A C: 24. Information and Alert: 20. Alert Response: 19. Control of Partitions: 13. Control of Lighting: 9. Control of Traffic: 9. Control of Electricity: 7. Control of Emergency Systems: 6. Information: 6. Control of Energy: 5. Control of Valve: 4. Control of Elevator: 1.Level 1 analysis of the six classification characteristics. Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 1 created by authors
The figure shows six pie charts arranged in three rows of two. The first chart on the top left is titled “Types of Infrastructure.” The data from the chart in the clockwise sense are as follows: Buildings: 44. Transportation: 23. Hospital: 23. Others: 6. Dams and Irrigation: 3. The second chart on the top right is titled “T R L Level.” The data in the clockwise sense are as follows: T R L 1: 1. T R L 2: 3. T R L 3: 4. T R L 4: 4. T R L 5: 6. T R L 6: 21. T R L 7: 29. T R L 8: 18. T R L 9: 13. The third chart in the middle left is titled “Publishing Year.” The data in the clockwise sense are as follows: 2006: 1. 2009: 1. 2010: 5. 2011: 3. 2012: 3. 2013: 3. 2014: 6. 2015: 6. 2016: 10. 2017: 11. 2018: 9. 2019: 13. 2020: 7. 2021: 11. 2022: 10. The fourth chart in the middle right is titled “Types of Triggering Logic.” The data in the clockwise sense are as follows: No Trigger: 8. Explicit Triggering: 88. Implicit Triggering: 3. The fifth chart on the bottom left is titled “Type of Sensing Technologies.” The data in the clockwise sense are as follows: Motion: 39. Temperature: 27. Distance: 14. Biosensor: 14. Light: 9. Air Quality: 9. Pressure: 9. Imaging: 8. N A: 6. Occupancy: 6. Humidity: 6. N E I: 4. Moisture: 4. Power: 3. Electrical: 1. The sixth chart on the bottom right is titled “Type of Response.” The data in the clockwise sense are as follows: Control of H V A C: 24. Information and Alert: 20. Alert Response: 19. Control of Partitions: 13. Control of Lighting: 9. Control of Traffic: 9. Control of Electricity: 7. Control of Emergency Systems: 6. Information: 6. Control of Energy: 5. Control of Valve: 4. Control of Elevator: 1.Level 1 analysis of the six classification characteristics. Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 1 created by authors
3.2 Level 2 analysis – Dual Interpolation
The Level 2 analysis offers a deeper understanding of the progression and integration of sensing technologies in responsive systems within the built environment. A key insight from this analysis is the relationship between sensing technologies, types of responsive systems, technology readiness levels (TRL), infrastructure applications, and publication trends. This interconnected evaluation reveals both historical trends and recent advancements in responsive technology. The analysis of sensing technologies over the years, as shown in Figure 2 (left), demonstrates that foundational sensors like motion, temperature, distance, and biosensors have been integral to responsive systems since the mid-2000s. This indicates an early recognition of their importance in adapting spaces to occupant needs, such as adjusting lighting or HVAC based on movement or temperature changes. On the other hand, sensors for air quality and moisture monitoring have only gained traction within the last decade, reflecting a growing emphasis on environmental sustainability and occupant health. The emergence of newer sensors in recent years suggests an increasing sophistication in responsive systems, moving beyond basic spatial adjustments towards more nuanced environmental monitoring. This shift aligns with heightened awareness of indoor air quality, energy efficiency, and occupant well-being in various types of infrastructure. Figure 2 (right) highlights the distribution of different sensors across various types of infrastructure, showing that general buildings are the primary application for all sensor types, except biosensors. This broad adoption in buildings is likely due to the diverse range of functions they serve—encompassing commercial, residential, and institutional settings—requiring flexible and adaptable systems. Hospitals, on the other hand, rely heavily on biosensors alongside other key sensors like motion, temperature, pressure, and humidity. This trend is intuitive given the healthcare sector’s need for precise environmental controls and real-time monitoring of both patients and conditions. Motion and temperature sensors help maintain safety and comfort, while pressure and humidity sensors are critical for infection control and patient comfort. In transport infrastructures, the diversity of sensor use is notable. While motion and distance sensors dominate, imaging, lighting, temperature, and power sensors are also prevalent. This variety reflects the complex requirements of transportation systems, where monitoring traffic flow, ensuring safety, managing lighting, and maintaining optimal environmental conditions are key to operational efficiency. The use of distance and motion sensors (27% and 15%, respectively) underscores their significance in traffic management, automated navigation, and safety alert systems. The analysis also shows that while sensors are employed across other types of infrastructure like dams, irrigation systems, and energy-harvesting systems, their use remains more marginal. This lower adoption could be due to the specific and narrow requirements of these systems, which may not need the same level of environmental adaptation and occupant responsiveness as buildings or transportation infrastructure. Nonetheless, the gradual incorporation of sensors in these areas suggests a growing awareness of the potential benefits of responsive technologies even in traditionally less adaptive sectors.
The figure shows two vertical bar graphs arranged side by side. In both graphs, the horizontal axis lists categories from left to right as follows: “Air Quality,” “Biosensor,” “Distance,” “Electrical,” “Humidity,” “Imaging,” “Light,” “Moisture,” “Motion,” “Occupancy,” “Power,” “Pressure,” “Temperature,” “N A,” and “N E I.” The left graph is titled “Type of Sensing Technologies versus Year.” The vertical axis ranges from 0 to 40 in increments of 5 units. Each category on the horizontal axis contains multiple stacked bars representing publication years. A legend on the right lists the years from bottom to top as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The highest bars are seen for “Motion,” reaching 37, followed by “Temperature,” reaching 27, while “Humidity,” “Occupancy,” and “N A” have moderate heights, reaching 6, and “Electrical” and “Power” show the lowest values of 1 and 3, respectively. The right graph is titled “Type of Sensing Technologies versus Infrastructure.” The vertical axis ranges from 0 to 40 in increments of 5 units. Each category on the horizontal axis contains vertically stacked bars representing infrastructure types. A legend on the right lists the infrastructure types as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bar corresponds to “Motion,” reaching 37, followed by “Temperature,” reaching 27, primarily contributed by “Buildings.” The shortest bars appear for “Electrical,” “Power,” and “N E I,” with values ranging from 1 to 4. Note: All numerical data values are approximated.Level 2 analysis of the interpolation between sensing technology and year (left), and sensing technology and infrastructure (right). Source: Figure 2 created by authors
The figure shows two vertical bar graphs arranged side by side. In both graphs, the horizontal axis lists categories from left to right as follows: “Air Quality,” “Biosensor,” “Distance,” “Electrical,” “Humidity,” “Imaging,” “Light,” “Moisture,” “Motion,” “Occupancy,” “Power,” “Pressure,” “Temperature,” “N A,” and “N E I.” The left graph is titled “Type of Sensing Technologies versus Year.” The vertical axis ranges from 0 to 40 in increments of 5 units. Each category on the horizontal axis contains multiple stacked bars representing publication years. A legend on the right lists the years from bottom to top as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The highest bars are seen for “Motion,” reaching 37, followed by “Temperature,” reaching 27, while “Humidity,” “Occupancy,” and “N A” have moderate heights, reaching 6, and “Electrical” and “Power” show the lowest values of 1 and 3, respectively. The right graph is titled “Type of Sensing Technologies versus Infrastructure.” The vertical axis ranges from 0 to 40 in increments of 5 units. Each category on the horizontal axis contains vertically stacked bars representing infrastructure types. A legend on the right lists the infrastructure types as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bar corresponds to “Motion,” reaching 37, followed by “Temperature,” reaching 27, primarily contributed by “Buildings.” The shortest bars appear for “Electrical,” “Power,” and “N E I,” with values ranging from 1 to 4. Note: All numerical data values are approximated.Level 2 analysis of the interpolation between sensing technology and year (left), and sensing technology and infrastructure (right). Source: Figure 2 created by authors
Figure 3 presents a comprehensive analysis of the interplay between response types, publication years, infrastructure applications, sensor types, and technology readiness levels (TRL) in responsive systems. The distribution of response types over the years indicates that foundational technologies, such as energy and HVAC control systems, have been prevalent for nearly 2 decades, illustrating their established role in creating adaptive built environments. In contrast, newer technologies like automatic valve control and information systems have gained traction more recently, with 25% of related papers published in 2017, signifying a shift towards modern, digital solutions in responsive design. The analysis reveals that most responsive systems are primarily designed for buildings and transportation infrastructures, which highlights the pressing need for automation in these areas to enhance operational efficiency and occupant comfort. Hospitals, however, show a distinct preference for automated information gathering and alert systems that operate without physical triggers. This choice likely stems from the critical need for rapid information sharing and decision-making in healthcare settings, where timely data can be pivotal in patient care and operational management. Within healthcare facilities, responsive solutions have emphasized timely alerts and patient safety. For instance, Lu et al. (2021) developed an innovative biosensor-based alert response system (TRL 5), capable of real-time monitoring of critical patient physiological parameters, thereby enhancing rapid response capabilities and patient care accuracy. Said et al. (2021) further contributed by implementing explicit-trigger information and alert systems (TRL 6) in hospitals, improving emergency preparedness through timely dissemination of critical information to healthcare providers. When examining the relationship between response types and sensing technologies, temperature and motion sensors emerge as the dominant technologies in building operations. For instance, HVAC control systems, along with information and alert responses, rely heavily on motion sensors, which account for about 25% of their total. The control of partitions and lighting also shows significant reliance on motion sensors, contributing to roughly 33% of their total response mechanisms. This reliance underscores the importance of real-time data and adaptive responses in managing indoor environments effectively. The findings regarding TRL further highlight the maturity of these technologies. A significant portion of the publications examined utilize responsive systems at least at the prototype testing stage (TRL 6), reflecting a well-established trend towards prioritizing practical testing and deployment over theoretical concepts. Notably, approximately 89% of HVAC control systems and 95% of information and alert response systems operate at a TRL of 6 or higher, indicating their readiness for real-world application. For alert response systems, this figure is 59%, suggesting a broad acceptance of these technologies in operational contexts. Remarkably, lighting control systems exclusively feature technologies at TRL 7 or higher, demonstrating advanced maturity and effective practical outcomes that enhance reliability and performance in lighting management. The clear prevalence of mature technologies may also be due to the fact that adopting solutions with lower levels of implementation maturity (i.e. TRL 1 to 6) triggers challenges such as institutional, regulatory, financial, and societal barriers, as recently highlighted by Aghimien et al. (2020) and Yadav et al. (2021).
The figure shows four vertical bar graphs arranged in two rows of two. In all the graphs, the vertical axis ranges from 0 to 25 in increments of 5 units. The horizontal axis in all the graphs lists categories from left to right as follows: “Alert Response,” “Control of Electricity,” “Control of Elevator,” “Control of Emergency Systems,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Control of Valve,” “Information,” and “Information and Alert.” The top left graph is titled “Type of Response versus Year.” In this graph, each category on the horizontal axis contains stacked bars representing publication years. A legend on the right lists the years from bottom to top as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The tallest bars are for “Control of H V A C” and “Information and Alert,” reaching 24 and 20, respectively, followed by “Alert Response,” reaching 19, while “Control of Elevator” has the lowest value of 1. The top right graph is titled “Type of Response versus Infrastructure.” In this graph, each category on the horizontal axis contains stacked bars representing infrastructure types. A legend on the right lists infrastructure types from top to bottom as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bars are for “Control of H V A C,” reaching 23, mainly represented by “Buildings,” and “Information and Alert,” reaching 20, mainly represented by “Hospital,” while “Control of Elevator” has the lowest value of 1. The bottom left graph is titled “Type of Response versus Sensing Technologies.” In this graph, each category on the horizontal axis contains stacked bars representing sensing technologies. A legend on the right lists sensing technologies from top to bottom as follows: “Air Quality,” “Biosensor,” “Distance,” “Electrical,” “Imaging,” “Light,” “Moisture,” “Motion,” “N E I,” “Occupancy,” “Power,” “Pressure,” and “Temperature.” The highest bars correspond to “Control of H V A C” and “Information and Alert,” reaching 24 and 19, respectively, with major contributions from “Motion” and “Temperature,” while “Control of Elevator” has the lowest value of 1. The bottom right graph is titled “Type of Response versus T R L.” In this graph, each category on the horizontal axis contains stacked bars representing T R L levels. A legend on the right lists T R L levels from bottom to top as follows: “T R L 1,” “T R L 2,” “T R L 3,” “T R L 4,” “T R L 5,” “T R L 6,” “T R L 7,” “T R L 8,” and “T R L 9.” The tallest bars are for “Control of H V A C” and “Information and Alert,” reaching 24 and 20, respectively, dominated by “T R L 7” and “T R L 8,” while “Control of Elevator” has the smallest value of 1. Note: All numerical data values are approximated.Level 2 analysis of the interpolation between type of response and year (top left), type of response and infrastructure (top right), type of response and triggering logic (bottom left), type of response and TRL (bottom right). Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 3 created by authors
The figure shows four vertical bar graphs arranged in two rows of two. In all the graphs, the vertical axis ranges from 0 to 25 in increments of 5 units. The horizontal axis in all the graphs lists categories from left to right as follows: “Alert Response,” “Control of Electricity,” “Control of Elevator,” “Control of Emergency Systems,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Control of Valve,” “Information,” and “Information and Alert.” The top left graph is titled “Type of Response versus Year.” In this graph, each category on the horizontal axis contains stacked bars representing publication years. A legend on the right lists the years from bottom to top as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The tallest bars are for “Control of H V A C” and “Information and Alert,” reaching 24 and 20, respectively, followed by “Alert Response,” reaching 19, while “Control of Elevator” has the lowest value of 1. The top right graph is titled “Type of Response versus Infrastructure.” In this graph, each category on the horizontal axis contains stacked bars representing infrastructure types. A legend on the right lists infrastructure types from top to bottom as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bars are for “Control of H V A C,” reaching 23, mainly represented by “Buildings,” and “Information and Alert,” reaching 20, mainly represented by “Hospital,” while “Control of Elevator” has the lowest value of 1. The bottom left graph is titled “Type of Response versus Sensing Technologies.” In this graph, each category on the horizontal axis contains stacked bars representing sensing technologies. A legend on the right lists sensing technologies from top to bottom as follows: “Air Quality,” “Biosensor,” “Distance,” “Electrical,” “Imaging,” “Light,” “Moisture,” “Motion,” “N E I,” “Occupancy,” “Power,” “Pressure,” and “Temperature.” The highest bars correspond to “Control of H V A C” and “Information and Alert,” reaching 24 and 19, respectively, with major contributions from “Motion” and “Temperature,” while “Control of Elevator” has the lowest value of 1. The bottom right graph is titled “Type of Response versus T R L.” In this graph, each category on the horizontal axis contains stacked bars representing T R L levels. A legend on the right lists T R L levels from bottom to top as follows: “T R L 1,” “T R L 2,” “T R L 3,” “T R L 4,” “T R L 5,” “T R L 6,” “T R L 7,” “T R L 8,” and “T R L 9.” The tallest bars are for “Control of H V A C” and “Information and Alert,” reaching 24 and 20, respectively, dominated by “T R L 7” and “T R L 8,” while “Control of Elevator” has the smallest value of 1. Note: All numerical data values are approximated.Level 2 analysis of the interpolation between type of response and year (top left), type of response and infrastructure (top right), type of response and triggering logic (bottom left), type of response and TRL (bottom right). Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 3 created by authors
3.3 Level 3 analysis – triple interpolation
The Level 3 analysis reveals critical relationships among response types, infrastructure categories, and their evolution over time, highlighting significant trends in the adoption of responsive systems. Consistent with the Level 1 findings, a substantial 84% of the reviewed research papers were published between 2014 and 2022, with notable spikes in activity post-2016. Sector-specific breakdowns provide further insights into these trends (Figure 4, left). In the building sector, the emphasis on HVAC control systems peaked in 2019, underscoring a heightened focus on energy efficiency and climate control. This trend reflects a broader industry shift towards sustainable building practices. Additionally, the increase in studies on adaptable partition controls highlights the necessity for flexible building layouts, particularly in urban environments where space optimization is essential. Such adaptability is increasingly relevant in light of changing workplace dynamics, such as the growing demand for flexible office spaces driven by remote work trends. In healthcare, research activity steadily increased until 2018, particularly around information and alert response systems. This focus stems from the urgent need for reliable patient monitoring and emergency alert mechanisms in hospitals, positioning these systems among the most prevalent and highlighting their early recognition by researchers. The documentation of automated information collection systems as early as 2009 underscores the long-standing acknowledgment of their significance in healthcare. Research on transport infrastructure spans a diverse range of response systems, with twelve distinct types identified. Krishnan et al. (2022) developed an explicit-trigger alert response system (TRL 8) using advanced motion sensing technologies to enhance safety within transportation networks. Their system was specifically designed to rapidly detect hazards, subsequently alerting personnel and passengers, significantly reducing response times and improving safety outcomes. The earliest studies date back to 2006 for energy control and 2009 for traffic management. In contrast, responsive controls for emergency systems and lighting only gained traction from 2020 onward, indicating a delayed yet growing interest in these areas. This shift suggests an evolving understanding of the importance of integrated systems in managing transport-related challenges. The analysis of Technology Readiness Levels (TRL) across different response systems reveals distinct trends by sector (Figure 4, right). Specifically, systems used in healthcare facilities, as well as traffic and energy control management, align with the trends identified in the Level 1 analysis. Notably, 17 out of 23 studies focusing on hospitals and clinics report TRL levels ranging from 6 to 8, indicating a strong level of maturity and readiness for deployment. Additionally, studies concerning electricity, lighting, and emergency control systems predominantly operate at TRL 6 or above. In contrast, many responsive systems developed for building operations are still in the experimental stages, primarily falling within TRL levels 2 to 5. This trend is particularly evident in alert response systems, HVAC controls, and partitioning technologies. While significant innovation is occurring in these areas, it highlights that many solutions are still undergoing refinement and testing before they can be considered fully mature and ready for implementation. This disparity underscores the need for continued research and development efforts to advance these technologies toward practical application.
The figure shows two vertical bar graphs arranged side by side. In both graphs, the vertical axis ranges from 0 to 24 in increments of 2 units. The horizontal axis lists five infrastructure types labeled from left to right as follows: “Buildings,” “Dams and Irrigation,” “Transportation,” “Hospital,” and “Others.” Each infrastructure type includes response categories from left to right as follows: “Buildings” include “Alert Response,” “Control of Electricity,” “Control of Elevator,” “Control of Emergency Systems,” “Control of Energy,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Valve,” and “Information.” “Dams and Irrigation” includes “Control of Emergency Systems” and “Control of Valves.” “Transportation” includes “Alert Response,” “Control of Electricity,” “Control of Emergency Systems,” “Control of Energy,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Control of Valve,” “Information,” and “Information and Alert.” “Hospital” includes “Alert Response” and “Information and Alert.” “Others” include “Alert Response,” “Control of Energy,” and “Information.” The left graph is titled “Infrastructure versus Type of Response versus Year.” Each category on the horizontal axis contains multiple stacked bars representing publication years. A legend below the graph lists the years from left to right as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The tallest bars appear for “Control of H V A C,” reaching 23 under “Buildings,” and “Information and Alert,” reaching 20 under “Hospital,” while “Control of Elevator,” “Control of Valve,” and “Information” under “Buildings,” “Control of Emergency Systems” under “Dams and Irrigation,” “Control of Partitions” under “Transportation,” and “Control of Energy” under “Others” show the lowest values of 1. The right graph is titled “Infrastructure versus Type of Response versus T R L.” Each category on the horizontal axis contains multiple stacked bars representing T R L levels. A legend below the graph lists the T R L levels from left to right as follows: “T R L 1,” “T R L 2,” “T R L 3,” “T R L 4,” “T R L 5,” “T R L 6,” “T R L 7,” “T R L 8,” and “T R L 9.” The tallest bars appear for “Control of H V A C,” reaching 23 under “Buildings,” and “Information and Alert,” reaching 17 under “Hospital,” both dominated by “T R L 7” and “T R L 8.” Moderate heights are observed for “Control of Lighting” under “Buildings” and “Control of Traffic” under “Transportation,” reaching 7 and 9 respectively. The lowest values are for “Control of Elevator,” “Control of Valve,” and “Information” under “Buildings,” “Control of Emergency Systems” under “Dams and Irrigation,” “Control of Partitions” under “Transportation,” and “Control of Energy” under “Others,” reaching 1. Note: All numerical data values are approximated.Level 3 analysis of the interpolation between infrastructure, response and year (left), infrastructure, response and TRL (right). Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 4 created by authors
The figure shows two vertical bar graphs arranged side by side. In both graphs, the vertical axis ranges from 0 to 24 in increments of 2 units. The horizontal axis lists five infrastructure types labeled from left to right as follows: “Buildings,” “Dams and Irrigation,” “Transportation,” “Hospital,” and “Others.” Each infrastructure type includes response categories from left to right as follows: “Buildings” include “Alert Response,” “Control of Electricity,” “Control of Elevator,” “Control of Emergency Systems,” “Control of Energy,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Valve,” and “Information.” “Dams and Irrigation” includes “Control of Emergency Systems” and “Control of Valves.” “Transportation” includes “Alert Response,” “Control of Electricity,” “Control of Emergency Systems,” “Control of Energy,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Control of Valve,” “Information,” and “Information and Alert.” “Hospital” includes “Alert Response” and “Information and Alert.” “Others” include “Alert Response,” “Control of Energy,” and “Information.” The left graph is titled “Infrastructure versus Type of Response versus Year.” Each category on the horizontal axis contains multiple stacked bars representing publication years. A legend below the graph lists the years from left to right as follows: “2006,” “2009,” “2010,” “2011,” “2012,” “2013,” “2014,” “2015,” “2016,” “2017,” “2018,” “2019,” “2020,” “2021,” and “2022.” The tallest bars appear for “Control of H V A C,” reaching 23 under “Buildings,” and “Information and Alert,” reaching 20 under “Hospital,” while “Control of Elevator,” “Control of Valve,” and “Information” under “Buildings,” “Control of Emergency Systems” under “Dams and Irrigation,” “Control of Partitions” under “Transportation,” and “Control of Energy” under “Others” show the lowest values of 1. The right graph is titled “Infrastructure versus Type of Response versus T R L.” Each category on the horizontal axis contains multiple stacked bars representing T R L levels. A legend below the graph lists the T R L levels from left to right as follows: “T R L 1,” “T R L 2,” “T R L 3,” “T R L 4,” “T R L 5,” “T R L 6,” “T R L 7,” “T R L 8,” and “T R L 9.” The tallest bars appear for “Control of H V A C,” reaching 23 under “Buildings,” and “Information and Alert,” reaching 17 under “Hospital,” both dominated by “T R L 7” and “T R L 8.” Moderate heights are observed for “Control of Lighting” under “Buildings” and “Control of Traffic” under “Transportation,” reaching 7 and 9 respectively. The lowest values are for “Control of Elevator,” “Control of Valve,” and “Information” under “Buildings,” “Control of Emergency Systems” under “Dams and Irrigation,” “Control of Partitions” under “Transportation,” and “Control of Energy” under “Others,” reaching 1. Note: All numerical data values are approximated.Level 3 analysis of the interpolation between infrastructure, response and year (left), infrastructure, response and TRL (right). Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 4 created by authors
Figure 5 illustrates the intersection of sensing types, response types, and infrastructure applications, revealing nuanced insights into how these elements interact within the context of responsive systems. A notable finding from the analysis is that many intersections correspond to only a single publication. For instance, only one study utilized imaging sensors for responsive partition control (Ariansyah et al., 2021), and another employed distance sensors for responsive traffic control (Mathur et al., 2010). This suggests that while certain sensing technologies are explored in various applications, many remain underutilized in specific contexts. Conversely, some sensing types are more prevalent in certain applications. For example, the use of air quality, motion, occupancy, and temperature sensors to activate responsive HVAC systems in buildings is more common, with counts of four, seven, five, and seven cases, respectively. This trend indicates a greater acceptance and reliance on established technologies that have demonstrated effectiveness in real-world settings. Furthermore, biosensors, distance sensors, motion sensors, temperature sensors, and pressure sensors are employed to initiate automatic information collection and alert systems, with respective counts of five, three, five, three, and three. Significantly, 16 of these 19 instances are centered in hospitals and clinics. These concentrated research applications can be attributed to two primary factors: first, the urgent need to automate critical services in environments like hospitals and general buildings, where efficiency and reliability are paramount. Second, the accessibility of mature sensing technologies—such as temperature and humidity sensors—facilitates their adoption in responsive systems. The integration of smart thermostats and other advanced technologies into HVAC control exemplifies this trend, as they leverage existing capabilities to enhance system responsiveness and improve energy efficiency. This interplay between sensing types and response applications underscores the importance of advancing research not only in established areas but also in less-explored intersections. By fostering innovation in underutilized technologies, researchers can expand the potential of responsive systems across various sectors, ultimately contributing to more adaptive and efficient built environment.
The graph is titled “Sensor Type versus Types of Response versus Infrastructure.” The vertical axis ranges from 0 to 8 in increments of 1 unit. The horizontal axis lists sensor types labeled from left to right as follows: “Air Quality,” “N E I,” “Biosensor,” “Distance,” “Imaging,” “Light,” “Moisture,” “Motion,” “Occupancy,” “N A,” “Power,” “Temperature,” and “Pressure.” Each sensor type includes response categories from left to right as follows: “Air Quality” includes “Control of Electricity,” “Control of H V A C,” and “Information.” “N E I” includes “Control of Emergency Systems,” “Control of Lighting,” “Control of Partitions,” and “Control of Traffic.” “Biosensor” includes “Alert Response” and “Information and Alert.” “Distance” includes “Alert Response,” “Control of Energy,” “Control of Traffic,” “Control of Valve,” and “Information and Alert.” “Imaging” includes “Alert Response,” “Control of Partitions,” and “Information.” “Light” includes “Alert Response,” “Control of Electricity,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” and “Control of Traffic.” “Moisture” includes “Control of Energy” and “Control of Valve.” “Motion” includes “Alert Response,” “Control of Electricity,” “Control of Emergency Systems,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Information,” and “Information and Alert.” “Occupancy” includes “Control of H V A C” and “Control of Traffic.” “N A” includes “Alert Response,” “Control of Electricity,” “Control of Partitions,” and “Information and Alert.” “Power” includes “Alert Response” and “Control of Electricity.” “Temperature” includes “Alert Response,” “Control of Energy,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Valve,” “Information,” and “Information and Alert.” “Pressure” includes “Control of Elevator” and “Information and Alert.” Each category on the horizontal axis contains multiple stacked bars representing infrastructure types. A legend below the graph indicates that the bars represent different infrastructure types as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bars appear for “Control of H V A C” under “Motion” and “Temperature,” reaching 7 and 6 respectively, while the smallest bars, reaching 1, appear for “Control of Electricity” under “Air Quality,” “Control of Valve” under “Distance,” “Control of Partitions” under “Imaging,” “Control of Energy” under “Moisture,” “Control of Traffic” under “Light,” “Control of Electricity” under “Power,” and “Control of Elevator” under “Pressure.” Note: All numerical data values are approximated.Level 3 analysis of the interpolation between sensor type, type of response and infrastructure. Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 5 created by authors
The graph is titled “Sensor Type versus Types of Response versus Infrastructure.” The vertical axis ranges from 0 to 8 in increments of 1 unit. The horizontal axis lists sensor types labeled from left to right as follows: “Air Quality,” “N E I,” “Biosensor,” “Distance,” “Imaging,” “Light,” “Moisture,” “Motion,” “Occupancy,” “N A,” “Power,” “Temperature,” and “Pressure.” Each sensor type includes response categories from left to right as follows: “Air Quality” includes “Control of Electricity,” “Control of H V A C,” and “Information.” “N E I” includes “Control of Emergency Systems,” “Control of Lighting,” “Control of Partitions,” and “Control of Traffic.” “Biosensor” includes “Alert Response” and “Information and Alert.” “Distance” includes “Alert Response,” “Control of Energy,” “Control of Traffic,” “Control of Valve,” and “Information and Alert.” “Imaging” includes “Alert Response,” “Control of Partitions,” and “Information.” “Light” includes “Alert Response,” “Control of Electricity,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” and “Control of Traffic.” “Moisture” includes “Control of Energy” and “Control of Valve.” “Motion” includes “Alert Response,” “Control of Electricity,” “Control of Emergency Systems,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Traffic,” “Information,” and “Information and Alert.” “Occupancy” includes “Control of H V A C” and “Control of Traffic.” “N A” includes “Alert Response,” “Control of Electricity,” “Control of Partitions,” and “Information and Alert.” “Power” includes “Alert Response” and “Control of Electricity.” “Temperature” includes “Alert Response,” “Control of Energy,” “Control of H V A C,” “Control of Lighting,” “Control of Partitions,” “Control of Valve,” “Information,” and “Information and Alert.” “Pressure” includes “Control of Elevator” and “Information and Alert.” Each category on the horizontal axis contains multiple stacked bars representing infrastructure types. A legend below the graph indicates that the bars represent different infrastructure types as follows: “Buildings,” “Dams and Irrigation,” “Hospital,” “Others,” and “Transportation.” The tallest bars appear for “Control of H V A C” under “Motion” and “Temperature,” reaching 7 and 6 respectively, while the smallest bars, reaching 1, appear for “Control of Electricity” under “Air Quality,” “Control of Valve” under “Distance,” “Control of Partitions” under “Imaging,” “Control of Energy” under “Moisture,” “Control of Traffic” under “Light,” “Control of Electricity” under “Power,” and “Control of Elevator” under “Pressure.” Note: All numerical data values are approximated.Level 3 analysis of the interpolation between sensor type, type of response and infrastructure. Since the years and TRL are progressive, the legends for these classification characteristics are color graduated to facilitate visualizing the progression, i.e. lighter regions signify more remote years or lower TRL, with values increasing as the shade darkens. Source: Figure 5 created by authors
4. Conclusions and further research
The systematic analysis of the current use of responsive systems for the built environment in the literature, presented in this paper provides the insights to the state of research and application and knowledge gap to cover in the field. Based on all three levels of analysis, several notable conclusions and implications emerge clearly:
Research in this domain has accelerated vigorously in less than a decade. Since 2016, there has been a discernible surge in the field, particularly with concern to the responsive control of HVAC and partitions in buildings, automatic alert response and automatic response systems for healthcare facilities, responsive control of traffic in transport infrastructure, and of energy and lighting on a mix of infrastructures. This growing trend highlights the urgent demand for innovative responsive solutions for the built environment, able of emphasizing safety, energy, and operational efficiency.
The technologies utilized are characterized by a high level of maturity across various sectors. TRL 6 or above consistently dominates over time, showing no clear trend indicating a shift towards either more mature or experimental technologies. Nevertheless, some experimental works (i.e. 1–5) are undergoing that use motion, imaging, and distance sensing to develop alert response and information response systems, particularly for buildings and transport infrastructure (Biloria, 2016; Lee et al., 2016).
The use on residential and commercial buildings, as well as transport hubs building such as train stations and airports stands out as one of the dominant testbeds for responsive systems, accounting for about half of the works analyzed. This prevalence seems to be at least partially justified by the greater availability of mature and relevant sensing technologies and actuators to develop automatic responsive HVAC and lighting systems. Healthcare facilities and transport infrastructures have also been largely involved in the development of responsive solutions.
A critical issue raised by recent research, such as the fuzzy synthetic evaluation by Aghimien et al. (2020) and the study by Yadav et al. (2021), underscores institutional, regulatory, financial, and societal barriers impeding the implementation of responsive systems in developing countries. The present review echoes these concerns, noting that systemic challenges, including regulatory frameworks, financial planning mechanisms, are critical to the adoption of technology, particularly these with lower level of implementation maturity (i.e. TRL 1 to 6). Addressing these barriers will be fundamental to accelerating the widespread application and integration of responsive technologies globally.
Furthermore, a crucial finding of this review emphasizes that effective integration of responsive technologies not only hinges upon technological maturity but also on interoperability and data integration capacities. The analysis indicates that most reviewed studies rarely address the interoperability challenges posed by combining multiple sensing and actuating platforms. Future research should focus explicitly on developing standardized protocols and robust frameworks that enable seamless integration across different responsive subsystems.
Importantly, while responsive systems clearly offer compelling operational and sustainability benefits, the associated costs and economic implications remain relatively underexplored. Future research should also investigate innovative financing models, public-private partnerships, and economic incentives capable of facilitating the wider adoption of responsive infrastructure solutions.
The present study identifies distinct opportunities for cross-sector integration, notably between building management systems and transportation infrastructure. Such integrations can facilitate urban-scale optimization of resource usage, operational efficiency, and enhanced resilience. However, this also requires careful consideration of complex governance structures, stakeholder alignment, and coordinated policies. Future interdisciplinary research is necessary to better understand and enable these cross-sector synergies effectively. Recent advancements in sensing technologies, moving beyond traditional temperature and motion sensors (Kumar et al., 2015), coupled with automatic response systems (Martani et al., 2017; Lai et al., 2022; Broennimann, 2023), are poised to drive the rapid proliferation of innovative sensing and response solutions. This evolution is expected to enhance operational efficiencies in buildings and contribute to more sustainable and resilient constructions and infrastructures. The integration of emerging sensing technologies—such as biosensors, wearables, and imaging sensors—with adaptive response mechanisms like AI-driven control systems could optimize built environments across various metrics: performance, cost efficiency, environmental sustainability, and user experience, particularly in critical service environments like healthcare facilities and transport infrastructures. While responsive systems present compelling opportunities for improving efficiency, sustainability, and resilience in the built environment, significant implementation barriers remain. Within this study, potential barriers to adopting responsive technologies have been thoroughly discussed including scalability across technical complexity and broader implementation challenges. Overcoming these barriers will be essential for the successful, widespread adoption of responsive technologies in diverse built environments.
However, the integration of these advanced technologies comes with significant costs that could hinder their feasibility and widespread adoption. A comprehensive analysis of these costs is essential for a realistic assessment of the viability of these solutions. Future research should focus on cost-benefit analyses to optimize these investments, aiming to identify strategies for reducing implementation costs without sacrificing effectiveness. Moreover, this study acknowledges limitations, including technological constraints, data accuracy issues, and interoperability challenges. Addressing these limitations will be crucial for paving the way toward more effective and scalable implementations of responsive systems, thus ensuring that the potential benefits can be fully realized. As a potential extension of the current work, the authors acknowledge that analyzing co-term occurrences, co-citation networks, and keyword clustering could provide deeper insights into the relationships and trends within the literature. Additionally, an in-dept analysis of the costs associated with different technologies could offer valuable perspectives on the financial constraints that hinder the adoption of sensing and response solutions.
This study does not involve human participants or animals.
