Explore the vast range of titles available.

Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects’ cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption.

Over the years, numerous studies have been conducted to investigate construction and demolition waste (CDW) management problems. However, the massive amount of literature brings challenges to scholars because it is difficult and time-consuming to manually identify research emphasis from the literature. Therefore, a method that can informationize literature collection and automatically detect insights from the identified literature is worthy of exploration. This paper attempts to present a comprehensive thematic model by combining Latent Dirichlet Allocation, word2vec, and community detection algorithm on python to detect insights from CDW management literature. Based on the database of Web of Science , 641 articles published between 2000 and 2019 are retrieved and used as the sample for analysis. The comprehensive thematic results reveal a four-domain knowledge map in CDW management research, which covers (1) introducing current situation of CDW management, (2) quantifying CDW generation, (3) assessing CDW and by-products, and (4) facilitating waste diversion. Future research directions in CDW management research have also been discussed. The results prove that the comprehensive thematic model is useful in mining insights from CDW management literature.

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model’s accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low.

Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design.

The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.

Integrating clustering with regression has gained great popularity due to its excellent performance for building energy prediction tasks. However, there is a lack of studies on finding suitable regression models for integrating clustering and the combination of clustering and regression models that can achieve the best performance. Moreover, there is also a lack of studies on the optimal cluster number in the task of short-term forecasting of building energy consumption. In this paper, a comprehensive study is conducted on the integration of clustering and regression, which includes three types of clustering algorithms (K-means, K-medians, and Hierarchical clustering) and four types of representative regression models (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Artificial Neural Network (ANN), and extreme gradient boosting (XGBoost)). A novel performance evaluation index (PI) dedicated to comparing the performance of two prediction models is proposed, which can comprehensively consider different performance indexes. A larger PI means a larger performance improvement. The results indicate that by integrating clustering, the largest PI for SVR, LASSO, XGBoost, and ANN is 2.41, 1.97, 1.57, and 1.12, respectively. On the other hand, the performance of regression models integrated with clustering algorithms from high to low is XGBoost, SVR, ANN, and LASSO. The results also show that the optimal cluster number determined by clustering evaluation metrics may not be the optimal number for the ensemble model (integration of clustering and regression model).

Compared with an ordinary prefabricated housing project (PHP), a transnational PHP tends to involve more uncertainties, with major stakeholders residing in different countries. This study proposes a novel digital project management framework that integrates building information modeling to enhance information utilization. This framework also incorporates innovative design concepts of modulor, modulus, module, model, durability, and recyclability for enhanced user comfort, housing industrialization, and extended lifespan. It was demonstrated how planning, design, manufacture, and transportation processes can be streamlined in transnational PHP delivery. A case study was performed in a typical transnational PHP between the Kingdom of Saudi Arabia and China for validation. By applying the framework, this PHP could install a single house within 24 h, improve precast level by about 20%, and reduce project cost per square meter by 5.2%, because of integrated design concept, reduced labor cost, effective material cost control, and enhanced information management.

While the construction and operation of subways have brought convenience to commuters, it has also caused ground subsidence and cracks of facilities around subways. The industry mainly adopts traditional manual detection methods to monitor these settlements and cracks. The current approaches have difficulties in achieving all-weather, all-region dynamic monitoring, increasing the traffic burden of the city during the monitoring work. The study aims to provide a large-scale settlement detection approach based on PS-InSAR for the monitoring of subway facilities. Meanwhile, this paper proposes a crack detection method that is based on UAVs and the VGG16 algorithm to quantify the length and width of cracks. The experimental data of Shenzhen University Section of Metro Line 9 are used to verify the proposed settlement model and to illustrate the monitoring process. The developed model is innovative in that it can monitor the settlement of large-scale facilities around the subway with high accuracy around the clock and automatically identify and quantify the cracks in the settled facilities around the subway.

There is an increase in decoration and renovation activities in the construction industry, and waste generation rates (WGRs) play a crucial role in guiding the management of demolition waste in decoration and renovation projects (DWDRPs). However, there has been little systematic research on this type of waste. Based on site surveys and a document review of 26 projects, this study offers insights into DWDRP wastes, from their initial generation to their final disposal. The results revealed that the WGRs for DWDRPs ranged from 30.96 kg/m2 to 629.96 kg/m2 and that the key components of DWDRPs included mortar, concrete, timber, tile, and metal; these five types of waste contributed 75.02% of the total waste. Although these findings deviate slightly from those of previous studies, these variations are attributed to diverse waste management practices, awareness levels, and employed construction technologies. Despite its importance, in China, the management of DWDRPs faces challenges, such as limited public awareness, inadequate collection and sorting guidance, and insufficient legislation. To counter these issues, we recommend a set of strategies, including stringent regulations, enhanced supervision, government incentives, improved collection and sorting methods, and the adoption of innovative technologies. This study not only sheds light on the specific challenges in decoration and demolition waste management in rapidly urbanizing areas but also proposes a comprehensive approach for improving waste management practices.

The continuous growth in building decoration activities has led to significant energy and material consumption, increasing carbon emissions in the construction sector. Existing literature frequently overlooks the carbon impact of building decorations. This study employs the life cycle assessment (LCA) method to quantify the carbon emissions associated with building decorations across five typic building types: residential, hospital, educational, sports cultural, and office buildings. Data were gathered using a mix of field investigations, document reviews, and semi-structured interviews, ensuring comprehensive coverage of all life cycle stages. The results reveal that carbon emission intensities of the studied building decorations ranged from 70.01 to 298.79 kg CO2 eq/m2, with the lowest emissions found in educational buildings and the highest in sports and cultural buildings. The decoration material production stage consistently emerges as the major contributor to emissions, accounting for over 50% of the life cycle of carbon emissions across all building types. The transportation stage also represents a significant share, contributing 18.6% to 24.5% across the building types. It also indicates that ceiling engineering as well as wall and column engineering are the primary carbon emission sources in terms of decoration activities. This study systematically compares the carbon emission characteristics of building decorations across multiple building types, addressing a gap that has been largely overlooked in the existing literature. It highlights the key sources of carbon emissions and proposes targeted mitigation strategies. The findings also suggest future research directions, including the application of innovative low-carbon materials, advanced construction technologies, and optimization of logistics. These insights lay a solid foundation for future low-carbon design and construction practices within the building sector.