Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves.

Construction companies are under pressure to enhance their site safety condition, being constantly challenged by rapid technological advancements, growing public concern, and fierce competition. To enhance construction site safety, literature investigated Machine Learning (ML) approaches as risk assessment (RA) tools. However, their deployment requires knowledge for selecting, training, testing, and employing the most appropriate ML predictor. While different ML approaches are recommended by literature, their practicality at construction sites is constrained by the availability, knowledge, and experience of data scientists familiar with the construction sector. This study develops an automated ML system that automatically trains and evaluates different ML to select the most accurate ML-based construction accident severity predictors for the use of construction professionals with limited data science knowledge. A real-life accident dataset is evaluated through automated ML approaches: Auto-Sklearn, AutoKeras, and customized AutoML. The investigated AutoML approaches offer higher scalability, accuracy, and result-oriented severity insight due to their simple input requirements and automated procedures.

Construction risk assessment (RA) based on expert knowledge and experience incorporates uncertainties that reduce its accuracy and effectiveness in implementing countermeasures. To support the construction of RA procedures and enhance associated decision-making processes, machine learning (ML) approaches have recently been investigated in the literature. Most ML approaches have difficulty processing dependency information from real-life construction datasets. This study developed a novel RA model that incorporates a graph convolutional network (GCN) to account for dependency information between construction accidents. For this purpose, the construction accident dataset was restructured into an accident network, wherein the accidents were connected based on the shared project type. The GCN decodes the construction accident network information to predict each construction activity’s severity outcome, resulting in a prediction accuracy of 94%. Compared with the benchmark feedforward network (FFN) model, the GCN demonstrated a higher prediction accuracy and better generalization ability. The developed GCN severity predictor allows construction professionals to identify high-risk construction accident scenarios while considering dependency based on the shared project type. Ultimately, understanding the relational information between construction accidents increases the representativeness of RA severity predictors, enriches ML models’ comprehension, and results in a more reliable safety model for construction professionals.

Predicting construction cost of rework (COR) allows for the advanced planning and prompt implementation of appropriate countermeasures. Studies have addressed the causation and different impacts of COR but have not yet developed the robust cost predictors required to detect rare construction rework items with a high-cost impact. In this study, two ensemble learning methods (soft and hard voting classifiers) are utilized for nonconformance construction reports (NCRs) and compared with the literature on nine machine learning (ML) approaches. The ensemble voting classifiers leverage the advantage of the ML approaches, creating a robust estimator that is responsive to underrepresented high-cost impact classes. The results demonstrate the improved performance of the adopted ensemble voting classifiers in terms of accuracy for different cost impact classes. The developed COR impact predictor increases the reliability and accuracy of the cost estimation, enabling dynamic cost variation analysis and thus improving cost-based decision making.

Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study deals with non-conformities proactively by adopting an AI-based predictive model approach. The main objective of this study is to provide an automated solution structured on the data recording system for the adverse impacts of construction quality failures. For this purpose, we collected 2527 non-conformance reports from 59 diverse construction projects to develop a predictive model regarding the cost impact of the quality problems. The first of three stages forming the backbone of the study determines crucial attributes linked to quality problems through a literature survey and the Delphi method. Secondly, the Analytical Hierarchy Process (AHP) and a Genetic Algorithm (GA) were used to determine the attribute weights. In the final stage, we developed models to predict the cost impacts of non-conformities, using Case-based Reasoning (CBR). We made a comparison between the developed models to select the most precise one. The results show that the performance of CBR-GA using an automated weighting model is slightly better than CBR-AHP based on a subjective weighting system, whereas the case is the opposite in standard deviation in forecasting the cost outcome of the quality failures. Using both automated and expert systems, the study forecasts the cost impact of failures and reveals the factors linked to poor record-keeping. Ultimately, we concluded that the outcome of non-conformities can be predicted and prevented using past events via the developed AI-based predictive model.

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO₂ emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas.