In an era where digital transformation is not just an option but a necessity, the integration of artificial intelligence (AI) and big data into the built environment represents a pivotal shift in civil engineering (Tein et al., 2022). The editorial panel’s choice to dedicate this themed issue to ‘Artificial intelligence and big data in the built environment’ stems from a profound recognition of their transformative impact. Far from just being tools, these technologies act as drivers of innovation, efficiency and sustainability in an area fundamental to society’s infrastructure.
Civil engineers are now entering a new era characterised by smart cities, automated construction and eco-friendly urban growth. AI and big data are at the forefront of this shift, bringing new levels of data analysis, predictive modelling and smart decision making (Calautit and Yong, 2023). This issue explores the core of this transformation, presenting studies that demonstrate how these technologies can enhance the efficiency, sustainability and robustness of the built environment.
The journey into this new phase starts with the article ‘Enhancing the detection performance of a vision-based occupancy detector for buildings’ by Tien et al. (2023). This study investigates AI-driven occupancy detection, using cutting-edge deep learning to transform building operation optimisation. It highlights the impact of precise occupancy detection on heating, ventilation and air conditioning (HVAC) systems and, consequently, on building energy usage. The paper demonstrates how AI’s accurate detection of and reaction to occupancy patterns can offer valuable insights into energy optimisation, a key factor in sustainable building. It sets the groundwork for a wider discussion on AI’s role in improving the operational efficiency and sustainability of built spaces.
Expanding on the topic of operational efficiency in building management, the next paper in the issue is ‘Influence of the equivalent electric load [EEL] strategy on energy demand forecasting’ by Vialetto and Noro (2023). This research extends from individual building operations to a broader view of energy management. It investigates the application of AI in improving the precision of energy demand predictions. The EEL strategy, supported by advanced AI algorithms, proves to be an essential element in aligning energy requirements with system operations. This study not only emphasises the importance of sustainable energy management but also highlights the pivotal role of AI in forecasting and controlling energy needs, thus contributing to the enhanced efficiency and sustainability of buildings.
The discussion broadens from building operations to larger infrastructure with the paper ‘Evaluating the impact of uncertainty events on the cost of linear infrastructure projects’ by Moghayedi and Windapo (2023). Utilising the adaptive neuro-fuzzy inference system (Anfis), this study highlights AI’s strategic importance in project management. It explores the assessment of cost uncertainties in infrastructure projects, a crucial aspect of planning and execution in civil engineering. The paper demonstrates AI’s exceptional abilities in predictive analysis and strategic decision making, extending its application from operational efficiency to strategic project planning. This represents a shift in our thematic exploration, connecting AI’s use in specific building contexts to its wider influence on the management of infrastructure projects.
Following this, the issue turns its attention to the expanding area of big data through the paper ‘Key drivers for big data adoption in the Dominican Republic construction industry’ by Reyes-Veras et al. (2023). This thought-provoking study connects AI with big data, highlighting the ongoing digital shift in the construction sector. It emphasises the increasing importance of incorporating big data into project management and operational workflows, showcasing its ability to enhance AI in transforming construction methodologies. The paper illustrates the co-operative interaction between AI and big data, demonstrating how their integrated application can bring substantial progress in the construction industry.
The issue concludes with ‘Benchmarking performance of machine-learning methods for building energy demand modelling’ by Erdem et al. (2023), returning to the topic of energy management in buildings. This study offers an in-depth comparison of different machine-learning techniques, showcasing the collaborative power of AI and big data in creating predictive models for energy usage. It revisits and strengthens the earlier conversation about sustainable building management, underscoring the potential of AI and big data in developing energy-efficient strategies. This paper connects the various themes discussed throughout the issue, underlining the essential contribution of AI and big data in promoting sustainable methods in civil engineering.
This themed issue of articles collectively showcases the transformative impact of AI and big data in civil engineering, offering a comprehensive view of their revolutionary role in the built environment. From enhancing building operations with AI-driven occupancy detection to managing infrastructure project costs using advanced AI and Anfis, the scope of AI’s influence is vast, leading to smarter, energy-efficient building-management systems and a leap towards sustainability. The issue also delves into the emerging role of big data in the construction industry, as seen in the Dominican Republic, where it emerges as a crucial tool for analytics and a driver of interconnected, efficient project management. The synergy between AI and big data is further highlighted in the analysis of machine-learning methods for building energy demand modelling, underscoring their combined potential in fostering energy-efficient, cost-effective and environmentally friendly building practices. These articles, offering visionary insights and academic contributions, herald a future where AI and big data are integral to developing sustainable, efficient and innovative civil engineering practices, inviting readers to engage with and contribute to the evolving discussion on these transformative technologies.
