Sustainability in the built environment is becoming a strong force in the construction industry to achieve social and environmental benefits and to lower negative environment impacts. The research objectives of this study were to identify the main drivers and barriers of sustainable design and construction in the US construction industry and to prioritise the identified drivers and barriers with respect to implementation of sustainability in the construction industry. To achieve the research objectives, this study identified the main drivers and barriers of sustainable design and construction factors with a two-step process, including an in-depth literature review and an experts' review. The identified drivers and barriers were then prioritised using a survey research method using a survey instrument developed by the authors. The sample used in the survey was comprised of members in the United States Green Building Council (USGBC) North Carolina (NC) Triangle Chapter. USGBC NC Triangle Chapter members were selected for the survey because of their interest in sustainable design and construction practices in North Carolina. After collecting data from the survey participants, the authors developed descriptive statistics to rank the main drivers and barriers of sustainable design and construction. The result indicated that the most important driving factors of sustainable design and construction were energy conservation, improving indoor environmental quality, environmental/resource conservation, and waste reduction. In addition, the most significant barriers to sustainable design and construction were first cost premium of the project, long pay back periods from sustainable practices, tendency to maintain current practices, and limited knowledge and skills of subcontractors. Based on these findings, the study suggests the direction of sustainability policies and programmes as well as the direction of future research and development.

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

Modular construction is an innovative new construction method that minimizes waste and improves efficiency within the construction industry. However, practitioners are hampered by the lack of environmental and economic sustainability analysis methods in this area. This study analyzes the embodied carbon emissions and direct construction costs incurred during the production phase of a modular residential building and provides comparison to an equivalent conventional residential building. Major drawings and design details for a modular residential building in South Korea were obtained, and the quantity take-off data for the major construction materials were analyzed for a modular construction method and a conventional construction method using a reinforced concrete structure under the same conditions. Focusing on major construction materials during the production phase, the embodied carbon emissions assessment revealed that adopting a modular construction approach reduced the environmental impact by approximately 36%, as compared to the conventional reinforced concrete method. However, in terms of the direct construction cost, the modular construction was approximately 8% more expensive than the conventional reinforced concrete construction method.

As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human–robot collaboration in construction.

The U.S. Green Building Council’s (USGBC) Leadership in Energy and Environmental Design (LEED) certification program supports sustainable construction as part of the effort to address climate change and resource depletion. It is the world’s most popular green building certification system, with more than 146,400 projects. Satisfying the LEED requirements brings many benefits to a project’s design performance and adds community value, but it does incur additional costs and challenges. This study examined the choices made by those working on the 222 LEED New Construction version 4 (LEED-NC-V4) projects that were certified between September 2014 and March 2020 to determine how the LEED project teams selected appropriate LEED goals. The results reveal interesting insights into the way project LEED goals and the credits corresponding to the target certification level were chosen, as well as the links and trade-offs between the various credit options. Based on these findings, useful suggestions are made for ways to help LEED project teams achieve their target certification levels and encourage authorities to continue to improve their local green regulations. The analysis of actual certified projects’ data makes it possible to re-evaluate the effects of newly updated requests in LEED v4 in the light of the stated goals of the USGBC.

The hotel industry is beginning to implement green design and construction practices, saving energy, water, and resources and thus helping to preserve the environment. In addition, green building practices also can provide healthy and comfortable indoor environments to hotel occupants including guests and employees. However, there is the potential for conflict between green building practices and hotel guests' satisfaction and comfort, as the conservation of resources could detract from the quality of a guest's visitor experience. This study adopted a case study approach to identify and analyze green design and construction practices that create a green and luxurious environment without damaging the hotels' financial position. An in-depth literature review was conducted to identify green design and construction practices, design features of premium hotels, and major design conflicts between the twin goals of green building and a luxurious hotel environment. Two LEED platinum hotels (the Proximity Hotel and the Bardessono Hotel, both in the United States) were selected and data collected on their green design and construction practices, luxurious design features, and operation and maintenance practices from multiple sources, including the owner, designer, contractor, engineer, and LEED consultant. From the perspective of the entire lifecycle of the building, this data was analyzed to identify green design and construction practices that not only provide a green, luxurious environment but also enhance the hotels' financial strength.