Accurate assessment of building energy consumption is essential to promote urban sustainability and low-emission cities. Building energy consumption is strongly influenced by the local weather conditions. Most building energy simulation tools in city-scale studies adopt the Typical Meteorological Year weather file, which is uniform in a city. However, the complex interplay between the local environment and man-made structures shapes the unique micro-climate conditions within a city. This study associates the interaction between building morphologies and wind environment by developing the building height distribution matrices in 8 directions. Deep transfer learning neural networks were designed to predict the micro-climate conditions with the wind-direction-dependent morphology extracted from these matrices. A validation experiment was executed at Southeast University by loading wireless sensor networks. The results of this experiment prove the efficacy of the proposed method. Compared with the model with conventional morphological factors, the prediction errors were decreased by 23% and 42% for temperature and relative humidity, respectively.

Accurate urban microclimate representation is essential for reliable building energy simulation (BES), yet conventional city-scale Typical Meteorological Year (TMY) weather files fail to capture intra-urban microclimate variability. Dense sensor-based monitoring could improve accuracy, but is impractical at the city scale. This study proposes a hybrid physics-based and data-driven framework for spatiotemporal urban microclimate prediction at non-monitored locations. The framework integrates the spatial prediction capability of a physics-based Urban Weather Generator (UWG) with the temporal prediction strength of a multimodal deep learning model (LSTM-Imagery), enabling accurate microclimate prediction using city-level weather data. The model is first trained and validated in Nanjing, and then adapted to Hong Kong and Singapore using a transfer learning strategy with limited fine-tuning data. The resulting air temperature prediction RMSEs are 1.644 °C, 1.262 °C, and 1.117 °C, respectively, demonstrating the framework’s accuracy and cross-climatic applicability. The results demonstrate that the proposed hybrid framework provides an accurate, scalable, and transferable solution for urban microclimate prediction, with strong potential to improve BES accuracy and urban climate assessment in data-scarce environments.

Accurate and robust localization is a fundamental requirement for service and inspection robots, particularly in feature-sparse indoor environments where traditional systems struggle due to a lack of distinct landmarks. While prior maps can enhance robustness, precise and compact maps capturing real-world details are often unavailable for new or frequently changing environments. This paper presents BIM-Loc, a novel discrepancy-aware LiDAR-based localization method that directly integrates Building Information Models (BIM) from the design phase. BIM-Loc simultaneously estimates trajectories aligned with the BIM coordinate system and identifies discrepancies between real-world observations and the as-designed BIM in an online fashion. Our core contributions include: (1) a novel multi-hit ray casting strategy for efficient BIM-point data association and projection of 3D observations into 2D texture space; (2) a pose graph optimization framework with BIM-integrated factors that enforces consistency among odometry, sequential scans, and BIM structures; and (3) a hierarchical Bayesian inference module that incrementally updates a continuous 2D surface representation for discrepancy detection, propagating updates from the pixel to the structure level. Extensive evaluations in both simulation and real-world applications demonstrate that BIM-Loc significantly outperforms state-of-the-art map-based methods in localization accuracy and robustness. More experimental results are available at our project website: .

The building sector is a major contributor to global energy use and carbon emissions, positioning it as a key target for decarbonization. Hydrogen, as a clean and flexible energy carrier, offers significant potential in supporting this transition. This review synthesizes recent advances in hydrogen applications for buildings, highlighting experimental findings, technological feasibility, socio-technical considerations, and policy developments. Key deployment models include hydrogen fuel cell integration, hydrogen-based microgrids, and combined heat and power systems. Emerging innovations such as renewable-powered hydrogen production, decentralized storage, and cross-sector integration with transport systems are also examined. In particular, the role of hydrogen-powered vehicles as energy buffers is emphasized for enhancing building energy autonomy. Despite its promise, hydrogen deployment faces technical, economic, and institutional challenges. The study calls for coordinated strategies that integrate hydrogen with other renewables, improve policy frameworks, and foster public acceptance. Future research should prioritize system integration pathways, techno-economic optimization, real-world demonstrations, and interdisciplinary collaboration. This review offers a strategic reference for scaling up hydrogen use in the built environment and underscores its critical role in achieving net-zero emissions and sustainable urban energy systems.

Large language models (LLMs) can generate optimization solver code from natural-language specifications, yet without domain guidance the generated programs frequently suffer from syntax errors, runtime exceptions, and physical-constraint violations. We present SBEMA (Smart Battery Energy Management Agent), a knowledge-driven LLM agent that addresses these reliability failures through four mechanisms: (1) a curated knowledge base encoding solver specifications, constraint templates, and known error patterns; (2) a progressive tool disclosure strategy that dynamically loads tools in three levels to maintain context focus; (3) a staged prompting strategy that decomposes generation context into task description, error warnings, domain knowledge, and instance data; and (4) a four-stage validation pipeline covering syntax, execution, output format, and physics, coupled with an autonomous error repair loop. A Best-of-3 (BO3) selection strategy further compensates for LLM stochasticity, raising daily feasibility from 69.9% per-run to 96.3%. On 100 representative home energy scheduling scenarios, the knowledge base proves indispensable (0.6% success without it vs. 66.8% with it), staged prompting improves feasibility by 8.8 percentage points, and the repair loop adds 10.6 percentage points. Ablation analysis confirms that each component provides a distinct and complementary contribution.

Current hydrogen delivery assessments exhibit incomplete supply chain coverage, insufficient carrier diversity, and oversimplified spatial analysis relying on hypothetical distances. This study addresses these limitations through comprehensive life cycle assessment across China's four major transport routes for 2030-2060, evaluating 12 delivery systems comprising four transportation modes (pipeline, truck, ship, ultra-high-voltage transmission) and six hydrogen carriers across 25 sub-routes with actual geographic constraints and provincial energy profiles. Results reveal that spatial heterogeneity in regional energy systems rather than transport distance constitutes the primary determinant of delivery emissions. For the same delivery mode, emissions varied markedly across provinces because of differences in electricity carbon intensity, from 0.64 kg CO(2)eq/kg H(2)eq for Pipeline-CGH(2) on Yunnan-Guizhou to 1.98 kg CO(2)eq/kg H(2)eq on Inner Mongolia-Shanxi in 2030. Non-optimal mode selection further caused large emission penalties, especially in high-carbon routes and in later years. UHV-electricity transmission achieves the lowest emissions across 88% of sub-routes (0.03-0.31 kg CO(2)eq/kg H(2)eq by 2060), while ship-based transport demonstrates competitive performance in only one of three waterway-accessible routes, with this advantage reversing by 2060 due to accelerating grid decarbonization. Lifecycle stage decomposition indicates delivery-stage emissions dominate truck-based systems (>60%), while post-delivery processing dominates carrier-based modes. These findings demonstrate that hydrogen infrastructure planning requires regionally-differentiated strategies: priority UHV deployment for northwestern-to-eastern routes where emission disparities between optimal and suboptimal modes are most substantial, flexible multi-modal approaches for southwestern routes, and selective waterway infrastructure investment. This spatially-explicit framework provides evidence-based guidance demonstrating that regional energy profiles fundamentally shape delivery system performance beyond conventional distance-based optimization.

Many studies have evaluated single-policy measures for decarbonizing China's light-duty passenger vehicle fleet, but few have systematically addressed policy interactions. Using a dynamic fleet-based life cycle greenhouse gas emission model, we assess six major policies under 1152 scenarios. We find that accounting for synergies increases mitigation potential by up to 19.3 Gt CO2 eq, raising the number of scenarios consistent with the 2 degrees C pathway from 3 to 449. The strongest synergies arise from the joint acceleration of vehicle electrification, lightweighting, and power sector decarbonization, while rigid demand-side restrictions tend to reduce overall effectiveness. These results underscore the importance of coordinated policy portfolios. We recommend prioritizing technology-driven strategies and complementing them with flexible demand-side measures to maximize mitigation outcomes.

Inter-organizational projects (IOPs) are increasingly adopted to address complex and knowledge-intensive tasks across diverse industries. Effective inter-organizational knowledge flow is crucial for IOPs success but is often hindered by weak relational foundations, divergent systems, and limited collaboration history. Although prior studies identify trust and relational quality as central enablers of inter-organizational knowledge flow, they largely treat these relational conditions as pre-existing rather than examining how they are formed. As a result, the contextual antecedents that shape relational foundations—particularly the various forms of organizational proximity—remain insufficiently theorized in the project management literature. This gap is especially salient in IOPs, where organizations often lack collaboration history and operate under divergent governance systems. Drawing on transaction cost theory and social capital theory, this study examines how six dimensions of inter-organizational proximity—geographical, cultural, institutional, technological, policy, and goal—affect knowledge flow in IOPs, and whether this relationship is mediated by relationship quality. A sequential mixed-methods approach was employed, combining a three-wave survey of 272 professionals with semi-structured interviews in five representative IOPs. The results reveal a clear hierarchy: goal and cultural proximity are the primary drivers of relationship quality and knowledge flow, while technological proximity exhibits a significant negative impact on knowledge flow, a paradox attributed to perceived competition. Furthermore, geographical proximity has a moderate effect, and policy/institutional proximities are important but insufficient. Crucially, relationship quality serves as the central mechanism that translates structural proximity into effective collaboration. By bringing a knowledge governance and organizational proximity perspective to IOPs, this study contributes to the project management literature on the structural antecedents of relational conditions and offers practical guidance for partner selection and knowledge governance in complex, multi-organizational environments.

With the increasing adoption of BIM in building operation and maintenance, generating as-built Building Information Models (BIMs) for existing buildings becomes a growing demand. Converting laser-scanned point clouds into as-built BIMs (i.e., “Scan-to-BIM”) in the indoor scenes holds significant potential for the industry demand. This paper reviews the current state of indoor Scan-to-BIM research through a survey of 109 publications from the past decade. In the bibliometric analysis, the research status and relevant hot topics are identified. In the technical analysis, the implementation path, related methods for key steps, and modeling results of indoor Scan-to-BIM are statistically analyzed. Based on these findings, recommendations concerning development foundations, directions, and contents are proposed to foster the advancement of indoor Scan-to-BIM workflows.

Metro systems serve as the backbone of urban mobility and are pivotal to the resilience of modern metropolises. However, metro networks are continuously evolving infrastructures, yet current resilience assessments predominantly rely on static snapshots, failing to capture how the network resilience evolves during expansion. Therefore, this study introduced an integrated resilience assessment framework characterized as dynamics of dynamics, evaluating the evolutionary trajectory of network resilience in the face of complex disruptions. We constructed temporal complex networks for 12 global cities, integrating serviceability with refined topological modeling. By subjecting these evolving networks to critical node, region, and line disruptions at bi-level intensities, we identified distinct patterns of resilience evolution. The results revealed the evolution of serviceability efficiency and dynamic resilience. Furthermore, the study provided a more nuanced perspective on the relationship between metro network configuration and resilience evolution. While connectivity is generally beneficial, our analysis suggested that it may not always guarantee enhanced robustness in all contexts. The findings underscore the value of looking beyond the static resilience of metro networks, and the proposed framework offers urban planners a prognostic tool to optimize metro network expansion to enhance resilience.

Creating geometric digital twins (gDTs) for as-built roads still has many limitations, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience. A novel scan-to-building information modeling (scan-to-BIM) framework is proposed for automatic road gDT creation based on semantically labeled point cloud data (PCD), which considers six asset types: road surface, road side (slope), road lane (marking), road/traffic sign, road/street light, and guardrail. The framework first segments the semantic PCD into spatially independent instances or parts, and then extracts the sectional polygon contours as their representative geometric information, stored in Java-Script Object Notation (JSON) files using a new data structure. Primitive gDTs are finally created from the JSON files using the corresponding conversion algorithms. The proposed method achieves an average distance error of 1.46 cm and a processing speed of 6.29 m/s on six real-world road segments with a total length of 1200 m.

Metro-led underground spaces (MUS) have gained significant importance in addressing deteriorating urban issues in high-density built environments. However, existing planning techniques for MUS lack could enable the increasingly complex spatial morphology and function assignment, resulting in poor performance of MUS development in an unintegrated manner. To bridge the research gap, an enhanced layout planning approach for MUS (ELPA-MUS) was systematically formulated. ELPA-MUS incorporated a digital interpretation framework for MUS layout, enabling simultaneous analysis of spatial morphology and function. The model transformed the layout planning task into a multi-objective optimization (MOO) problem with nine objective functions. The non-dominant sorting genetic algorithm III (NSGA-III) was employed to find the Pareto front in high dimensions. To enhance the practicality of ELPA-MUS, an ensemble method was proposed, combining subjective expertise and objective computational analytics. The model was applied to a case study in Jinan, China to demonstrate its applicability and rationality. Overall, the ELPA-MUS model provided a modifiable paradigm for intelligent layout planning of complex underground spaces and expanded the data-driven planning toolkits towards a more

Construction workers working at heights are prone to falls, and an older workforce exacerbates the problem. To lessen falling risks, workers at altitudes require favorable cognitive states, which may be influenced by age. Few studies have examined how age affects cognitive states associated with the risk of falls from heights from a human factor perspective. Therefore, this study investigated the effects of age differences on vigilance, mental fatigue, attention, task engagement, and height-related anxiety among construction workers involved in construction activities at heights. This study presented immersive high-altitude construction scenes through virtual reality and adopted a new research concept for analyzing electroencephalogram data. The results indicate that older workers had lower vigilance and more mental fatigue than younger workers during work at heights, but they had higher levels of attention and task engagement and less height-related anxiety than younger peers. Five cognitive states had varied effects on the risk of falls in the two age groups; mental fatigue was the most significant for older workers, and height-related anxiety was most significant for the younger workers. This research expands the body of knowledge in age-specific safety management by determining the influential mechanism of age on construction workers' cognitive states during work at heights. The research findings will be useful in developing customized safety training programs and management strategies to improve specific cognitive states of construction workers of different ages before working at heights.

Urban microclimates exhibit substantial spatial variability, yet conventional city-scale weather files often fail to capture these differences, leading to notable errors in building energy modeling. Data-driven microclimate prediction offers a promising alternative but is typically constrained by data scarcity at individual sites. This study proposes temporal graph convolutional networks (TGCNs) built on a morphological adjacency matrix (MAM) that incorporates spatial coordinates and urban morphological features to represent cross-zone microclimate relationships. This design allows external data to be selectively leveraged while maintaining site-specific microclimate characteristics and limiting noise from unrelated monitoring points. A campus-scale case study is conducted to examine prediction performance and its influence on building energy modeling. The TGCN model using morphological adjacency matrices (TGCN-MAM) demonstrates superior accuracy in predicting air temperature and relative humidity compared with a Gated Recurrent Unit (GRU) model trained on single-site data (GRU-single) and a GRU model trained on multi-site data (GRU-multi). Relative to these two baselines, it reduces air temperature root mean squared error (RMSE) by 15.1% and 6.9%, and reduces relative humidity RMSE by 15.0% and 17.1%, respectively. When applied to energy modeling for three buildings, the generated weather data from the TGCN-MAM model keeps cooling energy deviations below 4%. These results demonstrate the model's effectiveness in providing more accurate urban microclimate prediction and improving the reliability of building energy modeling in heterogeneous urban environments.

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects.

Work-related injury insurance (WRII) is essential for protecting workers' rights and enhancing workplace safety, yet its promotion in construction faces challenges. This study investigates WRII adoption barriers and facilitators in China's construction sector using a mixed-methods approach. Semi-structured interviews with 14 industry experts were conducted, followed by thematic analysis to identify key barriers. An analytic hierarchy process was then applied to prioritize these barriers. The findings reveal six main barriers: (1) cumbersome claim process, (2) incomplete law and regulation system, (3) weak right-protection awareness of construction workers, (4) weak legal consciousness of contractors, (5) unreasonable compensation treatment and fund management, and (6) difficulties in employment relationship identification. To address these challenges, the study proposes five targeted measures: (1) simplifying the claim process, (2) promulgating authoritative and detailed laws and regulations, (3) strengthening supervision and law enforcement, (4) strengthening advocacy, and (5) improving insurance compensation and fund management. These findings offer practical guidance for policy-makers to enhance WRII effectiveness in China's construction industry and provide insights applicable to other developing countries. Despite limitations related to expert judgment and sector focus, the study offers valuable recommendations for future research on cross-industry comparisons and digital solutions for WRII.

The building sector of China encounters more challenges in achieving net zero emissions, as embodied carbon (EC) constitutes a larger proportion of total carbon emission compared to developed countries. However, there is a lack of systematic reviews that comprehensively analyze the current research status, limitations, practices and challenges particularly in the context of EC emissions in China’s building sector. This review carried out a bibliometric evaluation and detail content of analysis of studies on China’s EC reduction technologies and practices in the building sector, highlighting research gaps. Key focus areas include are on concrete and steel used in urban high-rise prefabrication buildings, biomass materials used in rural houses, passive buildings in extreme weather zones, and choices of renovation and demolition. Challenges and strategies for minimizing EC emissions in the China’s building sector were also explored, providing valuable insight for researchers and practitioners in this field.

To address information asymmetry between firms and stakeholders, there are increasing country-level efforts to mandate environmental disclosures. Despite the growing focus on the role of mandatory environmental reporting, little is known about how such requirements affect the linguistic complexity of corporate environmental disclosures. This study fills this gap by focusing on corporate environmental disclosure strategies in the architecture, engineering, and construction (AEC) industry, one sector particularly sensitive to environmental concerns. Based on a staggered difference-in-differences design, this study investigates the impact of mandatory reporting on the linguistic complexity of AEC companies' environmental disclosures. Although such mandates typically do not specify how to disclose information, the results demonstrate that mandatory environmental reporting has a significant and negative impact on linguistic complexity. The interaction results suggest that the negative impact is more pronounced for firms with a higher number of independent directors on boards. The findings emphasize the significant role of mandatory environmental reporting in altering corporate disclosure strategies from a linguistic perspective, ultimately contributing to a more transparent and informative business environment.

Building fire safety equipment (BFSE) management is increasingly complex and time-consuming. The objective of this paper is to develop augmented reality (AR)-enabled systems for BFSE based on cognitive ergonomics theory and explore the impacts of AR interaction modes on enhancing inspection performance. An experiment was conducted with 48 participants divided into three groups: control group with no AR assistance, visual-based AR group, and audiovisual-based AR group. Results indicate that the developed AR applications improve work efficiency, with the audiovisual-based system achieving the best task performance in BFSE inspections. The developed AR applications reduced cognitive load during inspections, although participants using the audiovisual-based AR system reported higher cognitive load regarding time pressure compared to the visual-based group. The findings contribute to developing efficient, user-friendly BFSE systems and understanding AR interaction modes, further validating the role of audiovisual-based AR interactions in improving facility management efficiency as well as building inspection and maintenance.