Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"An Integrated Vision-Sensor Fusion Framework for Wheel-Level Vehicle Trajectory Reconstruction and Axle-Load Estimation","authors":"Zijian Wang, Guozhi Fu, Wenjing Xia, Ghim Ping Ong","doi":"10.1016/j.cacaie.2026.100239","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100239","url":null,"abstract":"Accurate axle-load monitoring is essential for pavement design, evaluation and maintenance, yet conventional point-based pavement weigh-in-motion systems remain sensitive to uncertainties in actual wheel-load positions. To address this limitation, this study develops a physically interpretable vision–sensor fusion framework that reconstructs wheel-level loading geometry from monocular roadside video and integrates it with embedded pavement responses for axle-load estimation under in-service traffic conditions. Roadside video was processed through radial distortion correction, homography projection, YOLOv8-based truck detection, and HRNet-based keypoint identification. To infer tire-pavement contact points that are difficult to observe directly from an oblique monocular view, a surrogate keypoint strategy was introduced to reconstruct wheel-path trajectories on the road plane. Raw visual results were refined using rigid-body geometry, longitudinal kinematic constraints, non-holonomic motion priors, and Kalman filtering, converting image detections into temporally stable and physically consistent wheel-level trajectories. These trajectories were coupled with a simplified tire-pavement contact model and a multilayer-elastic-theory-based inversion model to estimate tandem rear-axle loads from embedded pavement responses. Field validation showed close agreement between vision-derived axle-passing times and sensor peaks, with an average timing discrepancy of approximately 8 ms. Reconstructed vehicle dimensions showed controlled errors mostly within ±0.15 m and identified rear-axle loads ranged from approximately 20 to 100 kN. Independent response validation reproduced measured stress at an unused sensor location, with a mean peak discrepancy of approximately 0.008 MPa, whereas estimation that disregarded the reconstructed wheel paths and assumed the load to act directly above the sensor produced markedly larger deviations. Results show that monocular traffic vision can provide mechanics-compatible wheel-level loading information for wheel-path-aware axle-load monitoring and reliable interpretation of in-service pavement sensor data.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"228 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning-Based Framework for Automated Section Line Generation in BIM Models","authors":"Sohyun Kim, Joonho Jeong, Jaewook Lee, Kwangbok Jeong, Taehoon Hong, Jongbaek An","doi":"10.1016/j.cacaie.2026.100237","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100237","url":null,"abstract":"Despite increasing Building Information Modeling (BIM) adoption in the Architecture, Engineering, and Construction (AEC) industry, 2D drawings remain necessary as construction project deliverables. Previous studies on BIM-based 2D drawing automation have mainly focused on fixed auxiliary elements, such as dimension lines and grid lines, whose locations can be determined using predefined rules. However, section lines have flexible locations because their placement depends on the information designers intend to communicate through section views. In addition, AI-based methods for predicting such locations generally require manually labeled data, resulting in substantial preprocessing effort. To address these limitations, this study proposes a deep learning-based framework with two models for different stages. The AutoLabel model automatically generates labeled data from raw 2D drawings, whereas the AutoSection model learns section-line placement patterns from labeled data and generates section lines. A Dynamo-based parametric algorithm then applies the generated section lines to BIM models to create corresponding section views. The framework was evaluated using 199 2D drawings collected from residential and commercial buildings, with the final dataset comprising 3,184 images after data augmentation. AutoLabel generated labeled data with an average structural similarity index measure (SSIM) of 92.7% within 1.87 s per drawing. AutoSection, consisting of Pix2Pix-GAN and post-processing, achieved an average SSIM of 95.39% for section-line generation. The proposed framework extends BIM-based 2D drawing automation from rule-based placement of fixed elements to learning-based placement of flexible section lines. It also reduces manual effort in labeled-data generation and has the potential to improve productivity in 2D drawing generation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"26 1","pages":"100237"},"PeriodicalIF":11.775,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Component-aware 3D damage localization on Gaussian splatting for bridge inspection","authors":"Zuoxu Wang, Xiao Liang","doi":"10.1016/j.cacaie.2026.100236","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100236","url":null,"abstract":"Structural damage assessment in civil infrastructure increasingly relies on visual data, while conventional image-based approaches remain limited by viewpoint inconsistency and the lack of explicit, structured geometric representations for reasoning. To address these challenges, recent advances in 3D scene representations such as 3D Gaussian Splatting enable efficient and high-quality reconstruction, but their application in infrastructure inspection remains limited due to the lack of integrated semantic understanding. This study presents a probabilistic framework that augments 3D Gaussian primitives with component-aware damage fields, allowing semantic information to be embedded without replacing or overwriting the underlying appearance representation. To improve robustness under multi-view inconsistencies, a stochastic multi-view learning strategy is combined with a local cross-attention mechanism to effectively fuse geometric and appearance cues. In addition, a structural gating formulation enforces consistency between component and damage predictions. Experimental results on three real-world bridge scenes demonstrate improved segmentation accuracy and cross-view consistency, providing initial evidence for consistent and interpretable 3D damage representations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"77 1","pages":"100236"},"PeriodicalIF":11.775,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCRIM truck routing optimization under resource constraints for network-level friction data collection","authors":"Run Hu, Yanfeng Ouyang","doi":"10.1016/j.cacaie.2026.100235","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100235","url":null,"abstract":"Network-level pavement inspection is critical for data-driven pavement management, maintenance planning, and roadway safety enhancement. Pavement friction measurement is one of the most significant components of this inspection process as it’s closely related to traffic safety, and the Sideway-force Coefficient Routine Investigation Machine (SCRIM) trucks have gained popularity in continuously collecting such data. However, efficient SCRIM operations for network-level friction data collection remain challenging because trucks must incur deadheading in order to survey a set of prescribed roadway segments in the network, while satisfying a set of resource constraints related to water refill needs, data-saving requirements, and crew working-hour limits. In this paper, we propose a mixed-integer linear programming (MILP) formulation for the network-level SCRIM truck routing problem, with the objective of minimizing a weighted combination of cost components (i.e., project completion time, deadheading time, water refill time and other operation times). To solve full-scale instances efficiently, we also develop a customized destroy-and-repair and local search metaheuristic with an embedded dynamic programming subroutine. The destroy-and-repair and local search part searches over routing decisions on roadway survey sequences, while the dynamic programming subroutine schedules water refill, data-saving, and crew’s commute and rest activities. Hypothetical network experiments show that the proposed solution approach obtains optimal or near-optimum solutions on small cases, and notably outperforms two state-of-art commercial solvers (Gurobi and Hexaly) on a range of medium and large cases. Real-world case studies based on Kentucky Districts 8 and 10 show that the optimized routes significantly reduce the total operation cost as compared to a nearest-neighbor greedy heuristic and the current industry benchmark. In District 10 (with around 1000 miles of survey distances), for example, the total deadheading time is reduced by 55%–58% (corresponding to approximately 800 miles of saved deadheading distance) and the total data collection time is reduced by about 40%. These results indicate strong promise for the proposed routing and scheduling optimization methods to gain operational efficiency for large-scale network-level data collection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":"100235"},"PeriodicalIF":11.775,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Drone-Based Scan-to-BIM for Railway Infrastructure: An Interactive Immersive Framework for Macro-Geometric Change Visualisation","authors":"Dimitris Perikleous, Jesus Garcia-Gago, Alejandra Ospina-Bohórquez, David Aldea-Sanchez, Alberto Morcillo-Sanz, Diego Gonzalez-Aguilera","doi":"10.1016/j.cacaie.2026.100229","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100229","url":null,"abstract":"Efficient railway infrastructure inspection and monitoring are essential for safety, performance, and long-term sustainability. However, traditional inspection methods are time-consuming and labour-intensive. This case study presents an automated, end-to-end, and interoperable pipeline that transforms raw point cloud data into an infrastructure-aware BIM-based change visualisation environment by integrating aerial drone-acquired geospatial data within a unified multi-level and multi-layer modelling framework. High-resolution imagery is processed into dense 3D point clouds, which are transformed into BIM-ready models through a six-stage scan-to-BIM pipeline. The resulting models are integrated into an interactive immersive framework developed in Unreal Engine, enabling dynamic visualisation and interactive comparison between BIM and as-built states. A case study conducted on a railway section in Madrid, Spain, demonstrates the proposed workflow as a methodological framework for automated reconstruction and macro-geometric change visualisation. By integrating point cloud semantics, topologically consistent infrastructure representation, and BIM interoperability, the framework establishes an interactive immersive environment within Unreal Engine that enables monitoring and change identification between as-designed and as-built BIM states.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"70 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Optimization of Roadside Monitoring Sensor Placement and Environmental Editing for Cost-Effective Cooperative Perception","authors":"Yang Ma, Mingyuan Li, Yubing Zheng, Shuyi Wang, Jiajia Chen, Said M. Easa","doi":"10.1016/j.cacaie.2026.100230","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100230","url":null,"abstract":"Optimizing the placement of roadside monitoring sensors (RMS) is essential for enhancing cooperative perception (CoP) in intelligent transportation systems. However, existing approaches primarily rely on increasing sensor quantity or hardware specifications to mitigate occlusions, overlooking the potential of proactively modifying the physical environment. To address this gap, we propose a synergistic optimization framework that jointly coordinates RMS deployment and strategic environmental editing within a digital-twin environment reconstructed from real-world point cloud data. By treating static obstacles as controllable variables, we formulate a budget-constrained, dual-layer optimization problem. To solve it efficiently, we develop a knowledge-guided cooperative co-evolutionary algorithm (KGCCA) that integrates ray-tracing feedback and artificial potential field theory, forming a heuristic optimization strategy to identify severe occlusions and guide sensor placement. Extensive experiments on diverse urban scenes from the WHU-Urban3D point cloud dataset, with multiple independent runs, demonstrate that KGCCA generally achieves faster convergence and outperforms baseline methods. Moreover, combining standard-grade sensors with targeted environmental editing achieves perception fidelity, measured by intersection-over-union and trajectory deviation, comparable to or even exceeding that of premium-grade hardware deployed in unedited environments. This study offers a cost-effective paradigm for smart city infrastructure, demonstrating that strategic environmental management within a digital-twin environment can serve as a viable alternative to purely hardware-driven perception enhancement.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interface-Constrained PINNs for Chloride Transport Inversion and Corrosion-Initiation Prediction of Marine PHC Pipe Piles","authors":"Bokai Song, Lin Li, Weibing Gong, Hui Wang","doi":"10.1016/j.cacaie.2026.100228","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100228","url":null,"abstract":"Reliable durability assessment of prestressed high-strength concrete (PHC) pipe piles in marine environments requires accurate characterization of chloride transport parameters. However, these parameters are inherently layer-dependent because of centrifugal forming, and they are difficult to identify from limited field or experimental observations. This study proposes an inverse-to-forward spatiotemporal interface-constrained physics-informed neural network (IF-STIC-PINN) for identifying layer-dependent chloride diffusion coefficients from sparse short-term observations and subsequently predicting long-term chloride ingress and corrosion initiation. Chloride transport in PHC pipe piles is formulated as a radial convection-diffusion process in a centrifugally stratified annular domain. The governing equations, initial and boundary conditions, and interface continuity conditions are embedded into the neural-network training through physics-informed residuals and automatic differentiation. To represent material heterogeneity, separate subnetworks are constructed for the inner and outer layers of the PHC pipe pile, while interface constraints enforce the continuity of chloride concentration and total flux across the centrifugal-layer boundary. Temporal segmentation is further introduced to enhance the stability of long-term prediction. The effects of monitoring duration, sensor placement, spatial and temporal decomposition, and observation noise are systematically investigated. The results show that monitoring near the inner and outer exposure boundaries for approximately two to three years enables stable identification of layer-dependent diffusion coefficients. The proposed IF-STIC-PINN reconstructs the 100-year chloride concentration field with an <mml:math altimg=\"si5.svg\"><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> relative error of approximately 0.72% and predicts a corrosion initiation time of 26.79 years, compared with 26.72 years obtained from COMSOL simulation. Validation against four published chloride-ingress cases yields concentration errors of 1.60%–2.36% and corrosion-initiation-time errors below 1.05%. These results demonstrate that IF-STIC-PINN provides a physics-consistent, data-efficient, and robust computational framework for translating sparse short-term chloride observations into long-term corrosion-initiation assessment of marine PHC pipe piles.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"61 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based automatic pixel-level detection and severity classification of freeze-thaw damage in concrete dams","authors":"Hao Gu, Jun Shen, Yangtao Li, Yixiang Fang, Yifan He, Zihan Jiang, Yihan Li, Miao He","doi":"10.1016/j.cacaie.2026.100219","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100219","url":null,"abstract":"Freeze-thaw deterioration is a critical durability problem for concrete dams in cold regions, where repeated temperature fluctuation, moisture migration, and surface scaling may progressively weaken concrete cover and threaten long-term structural safety. Although computer-vision methods have been increasingly used for civil infrastructure inspection, most existing studies remain crack-oriented and provide limited support for the simultaneous pixel-level segmentation, physical quantification, and severity grading of multiple freeze-thaw damage morphologies. To address this gap, this study proposes an integrated image-based framework for automatic freeze-thaw damage identification and quantitative severity assessment of concrete dams. A standardized image database is first established through laboratory accelerated freeze-thaw tests on concrete specimens with different strength grades, initial defect states, and cycle stages. An adaptive preprocessing strategy combining pre-denoising, gray-level dynamic clipping, contrast/brightness enhancement, and post-enhancement denoising is then developed to improve the visibility of weak damage features under non-uniform illumination and complex surface textures. On this basis, an improved Mask R-CNN model is constructed by introducing feature-pyramid hierarchy enhancement, multi-scale RoI feature fusion, and a 56 × 56 high-resolution mask branch, enabling instance-level segmentation of spalling, voids, aggregate exposure, and cracks across different spatial scales. The segmented masks are further transformed into engineering-scale damage indicators through camera calibration, including void area ratio, aggregate exposure ratio, spalling area ratio, maximum void diameter, and surface roughness, and k-means clustering is used to establish data-driven severity grades. Experimental validation shows that the proposed preprocessing method increases image contrast by 10.29-27.30 and entropy by 0.24-0.59 while maintaining SSIM values of 0.705-0.876, indicating improved feature enhancement without severe structural distortion. In a field case involving 2,000 dam inspection images, a locally calibrated scale of 0.0913 mm/pixel is used for case-specific physical quantification under comparable imaging geometry, and the extracted spalling area ratios range from 47.06% to 87.95%, consistent with manual engineering inspection records. The k-means severity groups are validated against two independent references: the freeze-thaw cycle count, the standard mechanical deterioration index in the accelerated freeze-thaw testing protocol adopted in this study, and periodic engineering inspection records compiled by qualified dam-safety personnel for the field case, with which the classification results show strong consistency. The main contribution of this work is therefore the engineering-oriented integration of standardized imaging, a purpose-engineered Mask R-CNN architecture, calibrated physical damage indicators, and valida","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"59 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Informed Graph Neural Networks for Minimal Mass Design of Cable-Strut Structures","authors":"Yan Zhou, Yafeng Wang, Yaozhi Luo","doi":"10.1016/j.cacaie.2026.100210","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100210","url":null,"abstract":"This paper presents a topology-adaptive physics-informed graph-to-design neural network (PI-GNN) framework for rapid nonlinear minimal-mass design of cable-strut structures. Although direct nonlinear optimization can yield rigorous solutions for individual feasible designs, repeated case-by-case optimization becomes computationally expensive in topology-level exploration, where numerous candidate structures must be evaluated and infeasible layouts or poor initial guesses often lead to failed or slow convergence. To address this challenge, the proposed framework reformulates repeated nonlinear optimization as a reusable graph-to-design learning problem that directly maps candidate structural graphs to preliminary prestress and member-level sectional designs. Variable-size graph modeling enables a unified network to accommodate different structural configurations. A feasibility screening network first evaluates candidate structures, after which the design network predicts prestress levels and member-level cross-sectional variables. An auxiliary displacement network estimates structural responses under multiple load cases, enabling a differentiable mechanics evaluator to quantify engineering-constraint violations. These violations are incorporated into augmented-Lagrangian physics losses and backpropagated to guide the design network toward lightweight and mechanically admissible solutions. Numerical studies on a planar photovoltaic cable truss and a spatial Levy cable dome demonstrate the accuracy, physical consistency, and computational efficiency of the proposed method for topology-level design exploration.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A motion-region-based center frequency optimization method for Gabor wavelets in phase-based displacement measurement","authors":"Sida Ai, Zhenkun Li","doi":"10.1016/j.cacaie.2026.100205","DOIUrl":"https://doi.org/10.1016/j.cacaie.2026.100205","url":null,"abstract":"Phase-based displacement measurement has attracted increasing attention in structural vibration monitoring because of its robustness to illumination variations and high subpixel accuracy. The Gabor wavelet is one of the earliest and most widely used phase extraction methods for phase-based displacement measurement. When Gabor wavelets are used for phase extraction, measurement performance is highly sensitive to the center frequency, while its selection in most previous studies relies on comparison with ground truth. To overcome this limitation, this study proposes a motion-region-based center frequency optimization method for Gabor wavelets in phase-based displacement measurement. Numerical experiments on numerical videos with different target motion characteristics show that the optimal center frequency varies systematically with these characteristics. In particular, the results reveal a clear relationship between the optimal center frequency and the target motion region, with larger motion regions generally requiring lower center frequencies for accurate displacement measurement. Based on this finding and the physical interpretation of the Gabor wavelet, a physics-inspired relationship between the optimal center frequency and the motion region is established, and an iterative center frequency optimization method is developed accordingly. The proposed optimization method is validated through a laboratory shaker test and an outdoor seismic response measurement of a cold-formed steel wall structure.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"35 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}