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Net-zero energy building (NZEB), an initiative to address energy conservation and emission reduction, has received widespread attention worldwide. This study aims to systematically explore recent challenges in NZEB retrofit research through a mixed-method approach and provide recommendations and future directions. A review of 106 documents (2020–2024) retrieved from the Web of Science and Scopus databases found that the globalization of NZEB retrofit research is unstoppable. Assessment methods are diverse, ranging from modeling energy efficiency (using different software such as DesignBuilder 7.0, PVsyst 7.4, EnergyPlus 24.1.0, etc.) to multi-attribute decision-making methods (e.g., DEMATEL-AHP/ANP-VIKOR) and comparative analysis. Current assessment metrics are dominated by economic benefits (e.g., net present value, dynamic payback period, and total operating cost) and energy consumption (e.g., electricity consumption and generation), with less consideration of environmental impacts (e.g., carbon reduction), as well as comfort (e.g., thermal comfort and indoor comfort). The study found that current challenges mainly include "Low economic feasibility of retrofitting", "Building retrofit energy code irrationality", and "Insufficient understanding, communication, and trust between stakeholders". To overcome these challenges, the study also proposes a framework of strategies to address them, including (1) maximizing natural space, (2) introducing a tenant equity system, (3) upgrading waste management, (4) strengthening energy monitoring, (5) establishing complete life cycle mechanisms, (6) providing systemic solutions; (7) promoting the use of low-carbon building materials, and (8) increasing policy support. [ABSTRACT FROM AUTHOR]

Occupational injuries in the construction industry have plagued many countries, and many cases have shown that accidents often occur because of a combination of project participants. Assembled construction (AC) projects have received extensive attention from Chinese scholars as a future trend, but few studies have explored the interrelationships and potential risks of various stakeholders in depth. This study fills this research gap by proposing a multi-stakeholder AC risk framework. The study surveyed 396 stakeholders, then analyzed the collected data and created a risk framework based on Structural Equation Modelling (SEM) and the CRITIC weighting method. The results revealed that factors like "regular supervision is a formality," "blindly approving the wrong safety measures," and "failure to organize effective safety education and training." are vital risks in AC of China. Finally, the study validates the risk factors and the framework with 180 real-life cases, which shows that the proposed framework is theoretically grounded and realistic. The study also suggests multi-level strategies such as introducing AI-based automated risk monitoring, improving the adaptability of normative provisions to technological advances, and advancing the culture of project communities of interest to ensure AC's safe practices. [ABSTRACT FROM AUTHOR]

Global climate change has led to frequent natural disasters such as tsunamis and earthquakes, making offshore construction risky. In this paper, high-level papers from the Web of Science (WoS) were searched, and critical terms were identified and categorized using text-mining techniques. To ensure the resilience and safety of marine structures, we discuss the challenges of marine clays, marine eco-civilization construction, and disaster prevention databases. The recommendations presented provide valuable insights for engineers, researchers, and other stakeholders involved in marine construction projects. [ABSTRACT FROM AUTHOR]

As urbanization continues to grow around the world, the risks associated with construction are increasing. Scientific and practical risk assessments help reduce safety risks and achieve healthy, long-term growth, so there has been much research in this field. Through a review of the literature, this study aims to reveal the state and trends of research in the field of safety risk assessment. We searched 473 articles on construction risk assessment from the Web of Science (WoS) in the last decade, bibliometrically analyzed them, and then uncovered their significance using CiteSpace software (6.1. R6 (64-bit) Basic). The primary topics of conversation are countries, institutions, authors, and keywords, followed by references. According to the co-authorship analysis, the current research in this field is mainly from China, the USA, and Australia. Most influential authors currently have teaching or research positions at educational institutions; the most notable of which include Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Tsinghua University. They form a relatively close network of institutional cooperation. Based on the results of the co-term analysis, this study found that the current research hotspots are mainly focusing on "multi-objective optimization", "risk management", "mechanical characterization", "mental fatigue", "accident prevention", and many others. Data-driven, AI-assisted, and multi-stakeholder participation are the future trends in this field. [ABSTRACT FROM AUTHOR]

Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on "ergonomics", "machine learning", "computer simulation", and "wearable sensors". Secondly, the previous 80 factors contributing to hoisting risks were summarized from a "human–equipment–management–material–environment" perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT's interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research. [ABSTRACT FROM AUTHOR]

Climate change has become one of the leading problems around the world. The transport sector is one of the major contributors to climate change. At the same time, climate change is also affecting transportation facilities and travel behaviour. This study proposed a bibliometric approach to explore the structure evolution development trends of this knowledge domain with a broader search scope and more objective results compared with a manual review. A total of 4073 peer-reviewed articles were collected from the WoS core collection database to conduct scientometric analysis. The collaboration analysis shows that the US, China, and European countries dominate this field, and international organisations' and government agencies' reports on climate change form the basis of this research field. A total of 14 co-citation clusters were identified, and the research on climate change and transportation primarily focused on the topics of policy options, travel behaviour, the COVID-19 lockdown, environmental cost, and air quality. Keyword co-occurrence evolution analysis was also conducted to track the latest research trends. Based on the results, we propose trends in four areas for future research. This study provides a holistic knowledge map for climate change and transportation research's past, present, and future. [ABSTRACT FROM AUTHOR]

The construction sector is one of Yemen's most important economic pillars. Building information modelling (BIM) is a new information technology implementation that can create an intelligent digital design of buildings to support a variety of tasks and provides a wide range of benefits throughout the project life cycle. However, BIM is not widely embraced in Yemeni construction firms. Compared with other countries, Yemen presents a unique case for BIM adoption due to the ongoing war in the country, which will assist in rapid rebuilding processes. Thus, a complete and systematic investigation of the factors affecting BIM adoption in the Yemeni construction industry is required. This study utilises five categories of impacting factors: Technology, Process, Policy, People, and the Environment to model the strategic implementation for BIM in the Yemeni construction industry. A random sample was used to achieve homogeneity and increase the consistency and quality of data. Purposive sampling was used to choose participants for the framework validation. The data were analysed using partial least squares structural equation modelling (PLS-SEM), and the key factors influencing BIM adoption were determined and modelled. The results show multivariate results indicate a high correlation within the measurement model for all factors affecting BIM adoption in Yemen. In addition, the developed model was deemed to fit because the analysis result of the model's coefficient of determination test (R2) is BIM adoption having 0.437, Environment at 0.589, and People having 0.310, demonstrating high acceptance. Moreover, the results reveal a high correlation between policy and people (>0.50), while the environment significantly affected BIM adoption (0.304). Overall, the model illustrated how various factors influence BIM adoption. The created framework highlights the importance of understanding BIM adoption concepts and challenges in the Yemeni construction industry. It is believed that this study highlights the BIM implementation in developing countries such as Yemen and the possibility of implementing the proposed method in other countries to develop their own BIM implementation strategy. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects' boundaries, especially in complex urban settings. As a result, combining deep learning and object-based image analysis (OBIA) has become a new avenue in remote sensing studies. This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. The proposed classification model achieved the best accuracy, with 0.96 OA, 0.95 Kappa, and 0.96 mIoU in the training area and 0.97 OA, 0.96 Kappa, and 0.97 mIoU in the test area, outperforming several benchmark methods including Patch CNN, Center OCNN, Random OCNN, and Decision Fusion. The analysis of CNN variants within the proposed classification workflow showed that the HybridSN model achieved the best results compared to 2D and 3D CNNs. The 3D CNN layers and combining 3D and 2D CNN layers (HybridSN) yielded slightly better accuracies than the 2D CNN layers regarding geometric fidelity, object boundary extraction, and separation of adjacent objects. The Bayesian optimization could find comparable optimal MRS parameters for the training and test areas, with excellent quality measured by AFI (0.046, −0.037) and QR (0.945, 0.932). In the proposed model, higher accuracies could be obtained with larger patch sizes (e.g., 9 × 9 compared to 3 × 3). Moreover, the proposed model is computationally efficient, with the longest training being fewer than 25 s considering all the subprocesses and a single training epoch. As a result, the proposed model can be used for urban and environmental applications that rely on VHR satellite images and require information about land use. [ABSTRACT FROM AUTHOR]

During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images. [ABSTRACT FROM AUTHOR]

This review targets the BIM interface, the BIM multi-model approach, and the role of employing algorithms in BIM optimization to introduce the need for automation in the BIM technique, instead of complicating manual procedures in order to reduce possible errors. The challenge with adopting BIM lies in the limiting ability of computer-aided design (CAD) to generate a read-able and straightforward Revit by BIM, requiring the homogeneous data format to be generalized better and maintain a super data mod. Furthermore, the communication and management inter-face (CMI) faces some shortcomings due to limitations in its ability to recognize the role of the interface during the project construction phase. This review demonstrates several proposals to simplify the interface, in order to facilitate better communication amongst participants. The industry foundation class (IFC) model requires a new technique to unlock the potential future of intelligent buildings using the BIM multi-model approach integrated with the Internet of Things (IoT). Trials conducted to enhance the BIM model lack advanced methods for optimizing cost, energy consumption, labor, material movement, and the size of layout of the project, by utilizing heuristic, metaheuristic, and k-mean algorithms. The enhancement of BIM could involve algorithms to achieve better productivity, safety, cost, time, and construction frameworks. The review shows that some gaps and limitations still exist, especially considering the potential link between BIM and building management system (BMS) and the level of influence of the BIM-IoT prototype. Future work should find the best approach to solve facility management within the dynamic model, which is still under investigation. [ABSTRACT FROM AUTHOR]

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction. [ABSTRACT FROM AUTHOR]

Hoisting is an essential aspect of Industrial Building System (IBS) construction. Although research on hoisting safety in China has made strides to focus on "worker," "data," "task," "site," and "accident," there still needs to be more approaches based on multi-dimensional social system thinking. Therefore, the paper aims to fill this gap. We investigated 105 hoisting accidents in China and found that hoisting accidents occurred most frequently in China's southeast coastal region; truck-mounted cranes and tower cranes were the most common types of machinery involved in accidents; hoisting load off, capsizing of crane machinery, and workers falling from height are the three most common accident types; the average impact of a single hoisting accident is approximately RMB 2.43 million direct economic loss, 1.543 deaths and 0.829 injured. This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. By combining Rasmussen's risk framework with the characteristics of hoisting construction, six risk domains and thirty-six safety risk factors were identified. Finally, we used AcciMap technology to construct a qualitative IBS hoisting management model, which exhaustively presents the systematic levels and propagation paths of the influencing factors by the PDCA method. The research helps academics explore strategies to improve the safety of hoisting construction in IBS. Moreover, the study outcomes can inform the policy-making process towards promoting healthy and sustainable construction development., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)