Na Xu

The construction environment of wharf yards is highly complex, with significant risks of mechanical injuries, underscoring the need for effective monitoring and early warning systems. Existing methods, such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and You Only Look Once version 3 (YOLOv3), often suffer from limited accuracy and poor real-time performance, making them unsuitable for dynamic conditions. To address this gap, this study proposes a risk monitoring and early warning approach that integrates fuzzy inference with target recognition. Fuzzy inference was used to quantify and classify mechanical injury risks, while the You Only Look Once version 5 (YOLOv5) detection algorithm and Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) technique enabled accurate recognition and tracking of workers and machinery. By combining fuzzy logic–based risk levels with target recognition outputs, the system delivers real-time early warnings and countermeasure recommendations, providing a robust solution for enhancing human–machine safety in dynamic construction environments.

Urban underground space is increasingly being developed to alleviate surface land constraints and support low-carbon urban development. However, carbon emission reduction (CER) benefits remain inadequately quantified and are not comparable across underground infrastructure types, largely due to the absence of a unified assessment framework, inconsistent system boundaries, and the omission of multi-pathway mitigation mechanisms such as carbon capture and storage and biological sequestration. This study proposes a CER benefit assessment framework for urban underground space that integrates mitigation mechanism identification, pathway analysis, and benefit accounting, explicitly incorporating biological carbon sequestration, carbon substitution, and carbon capture and storage within a unified accounting structure. Accounting models are then established for three representative underground infrastructure systems: transportation, public and commercial services, and municipal utilities. Using Nanjing as a case city to operationalize and validate the proposed assessment framework, we estimate CER across multiple pathways and compare regional differences. The results indicate that underground transportation infrastructure provides the largest benefit (8.74 × 10⁵ tCO₂e per year), mainly driven by travel substitution and energy savings in station buildings. Underground public and commercial facilities achieve 6.64 × 10⁵ tCO₂e per year, dominated by green-building energy savings and geothermal integration. Municipal utilities contribute a smaller but strategically important reduction, as they provide a long-term carrier for carbon capture and storage and are structurally integrated within underground utility corridors, totaling 0.98 × 10⁵ tCO₂e per year citywide. Overall, the findings reveal differentiated mitigation mechanisms and spatial heterogeneity across underground infrastructure systems, providing a theoretical basis for optimizing urban spatial planning and informing low-carbon transition policies.

To achieve the “dual carbon” goals, green construction technologies (GCTs) are in transition from pilot projects to full-scale promotion in the Chengdu–Chongqing Economic Circle. Previous studies have largely overlooked the quantitative analysis of parameters influencing stakeholder decision-making and the consideration of risk preferences in the process of GCTs of application. Based on evolutionary game and prospect theory, this study establishes a tripartite evolutionary game model involving the government, owners, and constructors. Through model derivation and numerical simulation, it analyzes the strategic evolution and parameter sensitivity of each stakeholder at different lifecycle phases of GCTs. Results uncover a three-stage path: strategy adjustment range, fast convergence range, and slow convergence range. Government funds achieve peak efficiency in the fast convergence range. Owners react most strongly to incentives, contractors to cost changes. State-owned enterprises rely on policy signals, whereas private enterprises focus on market returns and risk expectations. Targeted promotion mechanisms and policy recommendations are proposed, offering a theoretical basis and practical route for precise government intervention and low-carbon transformation of the construction industry.

Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field. Subsequently, 4877 entities and 5783 relationships were extracted by means of the BERT-BiLSTM-CRF model and the BERT-CNN model, with F1 values reaching approximately 0.8. Finally, the Neo4j graph database was adopted for data storage, and a domain knowledge graph was constructed. Based on this graph, services such as knowledge association, knowledge retrieval, and intelligent question-answering were implemented. These services effectively addressed key challenges in information acquisition and decision support for on-site safety management, thereby significantly enhancing response efficiency and quality while strengthening overall safety management practices within the construction industry.

Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top-k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations.

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms.

To address the challenges in multimodal information integration and the inefficiency of knowledge transfer in the construction technology disclosure of deep foundation pit projects, an intelligent generation method based on graph rule reasoning and template mapping was proposed. First, a multi-level domain knowledge structure model was constructed by designing domain concepts and relationship types using the Work Breakdown Structure (WBS). Second, entity and attribute extraction was performed using regular expressions and the BERT-BiLSTM-CRF model, while relationship extraction was conducted based on text structure combined with the BERT-CNN model. For image and video data, cross-modal data chains were built by adding keyword tags and generating URLs, utilizing semantic association rules to form a multimodal knowledge graph of the domain. Finally, based on graph reasoning and template mapping technology, the intelligent generation of construction disclosure schemes was realized. The case verification results showed that the proposed method significantly improved the structural integrity, procedural logical consistency, parameter traceability, knowledge reuse rate, environmental compliance, and parameter compliance of the schemes. This method not only promoted the standardization and efficiency of construction technology disclosure activities for deep foundation pit projects but also enhanced the visualization and intelligence level of the schemes.

Unsafe behaviors among construction workers constitute a primary cause of safety accidents, with unsafe psychological states often triggering such behaviors. Focusing on collective anxiety issues, this study introduces the standardized anxiety scale (SAS) into the construction domain to investigate the influence mechanism of anxiety psychology on construction workers’ unsafe behaviors. A hypothesized model was established, incorporating organizational climate, safety competency, and job satisfaction as mediating variables, and demographic characteristics (gender, age, educational background, work experience, anxiety level) as moderating variables. Data collected from field surveys were analyzed using SPSS 22.0 and AMOS 24.0 for correlation analysis, variance analysis, and regression analysis, while structural equation modeling (SEM) was employed to validate the theoretical model and hypotheses. The results indicate that (1) anxiety psychology exhibits a significant positive correlation with unsafe behaviors; (2) organizational climate, safety competency, and job satisfaction mediate the transmission pathways between anxiety psychology and unsafe behaviors; (3) enhancing organizational climate, improving safety competency, and ensuring job satisfaction effectively reduce safety incident rates. Additionally, this study proposes preventive measures targeting anxiety psychology across three dimensions: external environmental controls, internal self-regulation, and direct anxiety intervention. These measures provide novel perspectives for effectively reducing the occurrence of unsafe behaviors among construction workers and advancing safety governance frameworks in the construction industry.

Safety risks management is a critical part during the subway construction. However, conventional methods for risk identification heavily rely on experience from experts and fail to effectively identify the relationship between risk factors and events embedded in accident texts, which fail to provide substantial guidance for subway safety risks management. With a dataset comprising 562 occurrences of subway construction accidents, this study devised a domain-specific entity recognition model for identifying safety hazards during the subway construction. The model was constructed by a Bidirectional Long Short-Term Memory Network with Conditional Random Fields (BiLSTM-CRF). Additionally, a domain-specific entity causal relation extraction model employing Convolutional Neural Networks (CNN) was also developed in thsi model. The constructed models automatically extract safety risk factors, safety events, and their causal relationships from the texts about subway accidents. The precision, recall, and F₁ scores of Metro Construction Safety Risk Named Entity Recognition Model (MCSR-NER-Model) all exceeded 77%. Its performance in the specialized domain named entity recognition (NER) with a limited volume of textual data is satisfactory. The Metro Construction Safety Risk Domain Entity Causal Relationship Extraction Model (MCSR-CE-Model) achieved an impressive accuracy, recall, and F₁ score of 98.96%, exhibiting excellent performance. Moreover, the extracted entities were normalized and domain dictionary was developed. Based on the processed entities and relationships processed by the domain dictionary, 533 domain entity causal relation triplets were obtained, facilitating the establishment of the directed and unweighted complex network and case database about the risks of subway construction. This research successfully converted accident texts into a causal chain structure of “safety risk factors to risk events,” providing detailed categorization of safety risks and events. Concurrently, it revealed the interrelationships and historical statistical patterns among various safety risk factors and categories of risk events through the complex safety risks network. The construction of the database facilitated project managers in conducting management decisions about safety risks.