... at the incident locations. Sixteen machine-learning algorithms were comprehensively compared for injury severity classification using chronological hold-outs, stratified cross-validation and feature-ablation. Findings A Logistic Regression classifier provided a strong balance of discrimination...

... behaviour is based on safety compliance and safety participation. This study employs machine learning to develop a model to predict safety participation behaviour. Design/methodology/approach A comparative machine learning framework using eight classification algorithms was employed to identify key...

... Ethereum Machine learning CNN This work represents a crucial step toward enhancing building safety and fostering accountability within the construction industry. Moving beyond fragmented approaches, the proposed platform does more than simply detect anomalies or store data; it establishes a direct...

... for intelligent solutions that balance occupant comfort and environmental impact. Design/methodology/approach A PRISMA-based systematic review identified studies integrating AI, machine learning, and digital twins for IEQ monitoring, prediction, and control, yielding 152 reviewed papers. Findings...

... textual data of the building performance guidelines into structured tabular data suitable for machine learning. Moreover, the visualizations of the structured floor layouts data reveal new insights as a result of analyzing the dataset. The Oriented Environmental Swiss Dwellings (O-ESD) dataset...

...A.J. Hernandez-Bautista; Claudia Eréndira Vázquez-Torres; A.J. Cetina-Quiñones; Felix Henriquez; Mario Escobar-Ochoa; A. Bassam Purpose Machine learning (ML), a branch of artificial intelligence (AI), enables prediction and decision-making by learning from data. This technique forecasts complex...

...Qiyu Liu; Maud Lanau; Johan Rootzén; Filip Johnsson Purpose This study aims to develop a generalizable machine learning pipeline that uses only two-dimensional (2D) data to predict building characteristics, specifically construction year and floor space. This addresses the data gaps in building...

... Publishing Limited Licensed re-use rights only Samad Sepasgozar can be contacted at: dr.samad.sepasgozar@gmail.com Artificial intelligence (AI) Machine learning Large language models Reinforcement learning Deep learning Building information modeling (BIM) Algorithms Architecture Energy...

...Ali Pakdel; Carol K.H. Hon; Sara Omrani; Johnny Kwok-Wai Wong Purpose Early-stage building design optimisation research often addresses environmental impact and cost separately, despite their interdependence. Many studies apply optimisation algorithms or machine learning models to minimise either...

...Juliana Croffi; Veronica Soebarto; David Kroll; Helen Barrie Purpose This paper presents a pilot study of a machine learning (ML) approach to predict occupants' satisfaction with the indoor environment in high-rise mixed-use buildings, aiming to validate a proof of concept for integrating ML...

...Sakibu Seidu; Daniel W.M. Chan; Margaret Damilola Oyewole; Nimesha Sahani Jayasena; Oyewole Oyesomo Purpose Digital technologies (DT) and machine learning (ML) offer significant opportunities for the construction industry (CI), particularly in climate resilience (CR) assessment. Despite...

... rights only Road optimization Artificial intelligence Machine learning Evolutionary algorithms Genetic algorithm Road cracks Pavement maintenance is one of the crucial areas in infrastructure management, where road safety, sustainability and functionality should be ensured along...

... to predict construction site transport demand from a combination of commonly available construction project- and context-related data features. Design/methodology/approach Machine learning (ML) models are applied to multivariate datasets, where findings show that GFA is the most important feature...

... statistical and machine learning tools. Design/methodology/approach A database was created with computer simulation data on the energy performance of 2048 building conditions generated by factorial combination of 10 parameters. Sensitivity analysis was performed to identify which parameters contribute...

... machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase...

...., 2022) and using DT to automate the monitoring and controlling of emissions (Arsiwala et al., 2023 ; Tahmasebinia et al., 2023). Digital twins AI for net zero Machine learning Decarbonisation pathways Emission analytics Digital ecosystem Sustainable built environment...

... comfort Simulation Temperature prediction Machine learning EnergyPlus Studies reveal that excessive interior heat attribute to 50–85% of fatalities during severe heat extremities (Cadot et al., 2007 ; Fouillet et al., 2006). Thermal aspects of the design of a residential...

... to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability. Originality/value This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses...

... the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability...

... of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies. Design/methodology/approach This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant...