Xinghua Chen

Earthquake and post-earthquake fire cascading hazards impose disproportionate risks on dense informal historic districts across South Asia. Conventional multi-hazard evaluation methodologies fail to accommodate non-engineered mixed-use constructions and ultra-compact unplanned urban morphologies prevalent in regional megacities, owing to two inherent limitations: decoupled quantification of seismic deterioration and structural fire performance, and fire spread parameters calibrated for regular Western urban grids rather than narrow, congested historic streetscapes. This work develops a localized probabilistic risk framework integrating seismic damage-induced fire degradation, and advances two dedicated methodological improvements to address the identified research voids. Seismic damage-dependent fire resistance reduction coefficients are embedded within cellular automaton iterations to dynamically modulate effective burnout durations of seismically compromised buildings; urban morphological correction factors are further incorporated to refine inter-building fire propagation probabilities tailored to compact informal settlements. Field inventories, nonlinear pushover finite element analysis, morphology-modified fire simulation and large-sample Monte Carlo stochastic sampling are integrated to execute full-process quantitative risk assessment, with model calibration and validation conducted against field survey data and the 2019 Chawkbazar chemical fire archive in Dhaka. The empirically validated framework (note: the validation primarily applies to the fire spread submodel; the coupled seismic–fire mechanism is supported by numerical simulation rather than empirical observation) delivers transferable quantitative benchmarks for multi-hazard governance of analogous South Asian historic agglomerations, and provides actionable technical evidence to inform targeted urban renewal schemes and the formulation of earthquake-fire coupled design specifications within Bangladesh’s national building regulatory codes.

1. Modern civil engineering spatial structures are evolving towards larger scales, greater complexity, and refined designs, with large-span and expansive buildings emerging as hallmarks of urbanisa...

Accurate prediction of temperature fields in parallel wire cables—critical components like main cables, hangers, and stay cables in cable-supported bridges—is pivotal for their fire resistance assessment. However, existing studies predominantly focus on small-scale cables and rely on oversimplified homogeneous models that neglect internal cavities, undermining reliability for large-scale applications. Addressing this gap, this study investigates sectional heat transfer characteristics of large-diameter (100–340 mm) parallel wire cables with explicit consideration of internal cavities. Temperature rise tests were conducted to capture wire temperature histories at different cross-sectional positions, followed by the development of a refined numerical model incorporating cavity structures, validated using experimental data. The effects of fire source models, cable porosity, and diameter on heat transfer were analyzed. Results show numerical-experimental discrepancies ≤ 35℃ within 60 min and ≤ 10% thereafter, confirming model reliability. Internal cavities induce uneven temperature distributions, influenced by fire source, porosity, and diameter. HC fire causes peak temperatures of 1035℃ and gradients of 445℃, with the time to reach the peak values being 48 min and 12 min, respectively. That is, HC fire poses the greatest threat to cable components, and it is recommended as the fire source model for fire resistance analysis of cable components. Marked differences exist between homogeneous round steel and actual cables (357℃ in internal wire temperature, 402℃ in gradient), underscoring the necessity of cavity-aware modeling. The research provides reliable temperature rise data for evaluating fire-induced bearing capacity degradation and optimizing fire protection design of cable components.

To enhance crack detection accuracy during UAV-based inspections and address key challenges such as false positives from complex backgrounds, missed narrow cracks, and insufficient structural continuity modeling, this study proposes CFM-Net, a task-oriented segmentation network integrating Channel-Spatial Attention and Multi-Scale Structural Enhancement. Constructed on an optimized U-Net backbone, it employs three dedicated modules: the Channel and Spatial Attention Module (CBAM) to amplify crack-related features and suppress background interference; the Gated Fusion Module (GFF) to dynamically fuse multi-level features, improving detection of fine, narrow cracks; and the Morphology-Guided Multi-Scale Structural Perception Module (MGMSIB), designed to model the structural continuity and multi-scale characteristics of cracks. Comprehensive evaluations on the Mix Bridge Crack dataset demonstrate CFM-Net achieves competitive performance among the evaluated methods, with an mIoU of 80.05% and an F1-score of 87.06%. This represents a significant improvement over strong baselines, outperforming DeepCrack and CrackFormer by 2.3% and 2.42% in mIoU, and 1.21% and 1.03% in F1-score, respectively. Furthermore, the model demonstrates robust performance on heterogeneous crack datasets composed of multiple public sources, particularly in reducing false alarms, recovering narrow cracks, and maintaining crack topology. These results conclusively validate the effectiveness and practical utility of the proposed method for automated bridge crack inspection.