Computer-Aided Civil and Infrastructure Engineering

Papers
(The H4-Index of Computer-Aided Civil and Infrastructure Engineering is 51. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-08-01 to 2026-08-01.)
ArticleCitations
163
160
Cover Image, Volume 39, Issue 7141
Cover Image, Volume 39, Issue 7137
Cover Image, Volume 37, Issue 14115
Introduction110
Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques104
Cover Image, Volume 38, Issue 599
Issue Information - TOC98
Issue Information94
Multi-stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification93
Issue Information92
Cover Image, Volume 40, Issue 1984
Genetic algorithm optimized frequency‐domain convolutional blind source separation for multiple leakage locations in water supply pipeline84
Aggregation formulation for on‐site multidepot vehicle scheduling scenario83
Cover Image, Volume 38, Issue 980
Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection79
A maintenance-aware machine learning framework for network-level highway pavement condition prediction78
Infrastructure deterioration modeling with an inhomogeneous continuous time Markov chain: A latent state approach with analytic transition probabilities78
Rapid regional assessment of post‐hazard structures and transportation infrastructure using aerial images77
Using machine learning to analyze and predict construction task productivity74
Simulation of mixed traffic with cooperative lane changes73
Assessing the reliability of surrogate-based inverse identification beyond forward accuracy: A diagnostic framework for pavement systems73
Tiny-Crack-Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks71
A deep learning framework based on improved self‐supervised learning for ground‐penetrating radar tunnel lining inspection71
A dynamic neural network model for the identification of asbestos roofings in hyperspectral images covering a large regional area66
Aeroelastic force prediction via temporal fusion transformers66
A structure‐oriented loss function for automated semantic segmentation of bridge point clouds66
Vision‐based fatigue crack automatic perception and geometric updating of finite element model for welded joint in steel structures65
Multi‐objective optimization of nonlinear passive control systems for seismic response mitigation of bridges62
A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images62
Real‐time anomaly detection in construction equipment operations using unsupervised audio signal processing61
Record length coefficient in up-crossing rate analysis for design wind velocities60
Traffic signal optimization for emissions mitigation in urban road networks with contraflow left‐turn lanes60
Integrating triple attention convolutional network with multi‐objective optimization for excavation‐induced deformation prediction59
Issue Information59
Issue Information58
Cover Image, Volume 39, Issue 1657
Issue Information57
An automation solution to convert CAD engineering drawings into railroad station models55
Cover Image, Volume 39, Issue 655
Issue Information54
Issue Information54
53
Issue Information53
Cover Image, Volume 40, Issue 2053
Deep spatial‐temporal embedding for vehicle trajectory validation and refinement52
52
52
A coordinated ramp metering framework based on heterogeneous causal inference51
A 2D‐connected automated vehicle car‐following control algorithm51
A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage51
Network models for temporal data reconstruction for dam health monitoring51
Training of construction robots using imitation learning and environmental rewards51
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