Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and $R^2$) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

This research explores the application of digital twin technologies to progress the United Nations’ Sustainable Development Goals (SDGs) within the water-energy-food-environment (WEFE) nexus management in urban refugee areas. The study in Irbid Camp utilizes a detailed 3D Revit model combined with real-time data and community insights processed through advanced machine learning algorithms. An examination of 450 qualitative interviews indicates an 80% knowledge level of water conservation practices among the community but only 35% satisfaction with the current management of resources. Predictive analytics forecast a 25% increase in water scarcity and an 18% surge in energy demand within the next ten years, prompting the deployment of sustainable solutions such as solar energy installations and enhanced rainwater collection systems. By simulating resource allocation and environmental impacts, the digital twin framework helps in planning urban development in line with SDGs 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). This investigation highlights the capacity of digital twin technology to improve resource management, increase community resilience, and support sustainable urban growth, suggesting its wider implementation in comparable environments.

This study explores the strategic integration of Building Information Modeling (BIM) and Virtual Reality (VR) within the built environment, addressing the growing complexities of urban development. Through a detailed examination of BIM applications, the research highlights a 37 % reduction in design conflicts and iterations, thereby enhancing design precision and efficiency in urban construction projects. Simultaneously, VR technology is demonstrated to increase stakeholder engagement by 62 % and improve spatial awareness by 48 %, fostering greater community participation and inclusivity in the development process. The combined use of BIM and VR optimizes not only construction workflows but also considerably enhances environmental and socio-economic outcomes, such as a remarkable 20 % reduction in greenhouse gas emissions. It will, therefore, support advanced urban infrastructure development and provide a replicable model for sustainable and resilient urban planning. This research underlines the crucial contribution of integrating BIM and VR technologies to the development of sustainability and resilience of the built environment.

As a fundamental feature of green building cost forecasting, external support is crucial. However, minimal research efforts have been directed to developing practical models for determining the impact of external public and private support on green construction projects’ costs. To fill the gap, the current research aims to develop a mathematical model to explore the balance of supply and demand under deflationary conditions for external green construction support and the accompanying spending adjustment processes. The most current datasets from 3578 green projects across Northern America were collected, pre-processed, analyzed, post-processed, and evaluated via cutting-edge machine learning (ML) techniques to retrieve the deep parameters affecting the green construction cost prediction process. According to the findings, public and private investments in green construction are projected to decrease the cost of green buildings. Furthermore, the impact of public and private investment on green construction cost reduction during deflationary periods is more significant than its influence during inflation. As a result, decision-makers may utilize the suggested model to monitor and evaluate the yearly optimal external investment in green building construction.