Computer-Aided Civil and Infrastructure Engineering

(The H4-Index of Computer-Aided Civil and Infrastructure Engineering is 38. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 500 papers]. The publications cover those that have been published in the past four years, i.e., from 2019-12-01 to 2023-12-01.)
Automated pavement crack detection and segmentation based on two‐step convolutional neural network170
Deep learning for data anomaly detection and data compression of a long‐span suspension bridge144
Generative adversarial network for road damage detection133
Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system115
Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine105
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles87
A deep‐learning‐based computer vision solution for construction vehicle detection85
Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure77
Uncertainty‐assisted deep vision structural health monitoring76
Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption73
Deep reinforcement learning for long‐term pavement maintenance planning73
Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network72
Cross‐scene pavement distress detection by a novel transfer learning framework71
Real‐time regional seismic damage assessment framework based on long short‐term memory neural network70
Noncontact cable force estimation with unmanned aerial vehicle and computer vision68
Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning61
Applicability of machine learning to a crack model in concrete bridges57
Crack detection using fusion features‐based broad learning system and image processing56
Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks54
Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot54
Vibration‐based semantic damage segmentation for large‐scale structural health monitoring53
Efficient training of physics‐informed neural networks via importance sampling52
Automatic railroad track components inspection using real‐time instance segmentation52
Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold51
Semi‐supervised learning based on convolutional neural network and uncertainty filter for façade defects classification50
Hybrid deep learning architecture for rail surface segmentation and surface defect detection50
Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network48
Deep learning for post‐hurricane aerial damage assessment of buildings47
Balanced semisupervised generative adversarial network for damage assessment from low‐data imbalanced‐class regime46
Pavement defect detection with fully convolutional network and an uncertainty framework45
Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras43
Structural damage detection and localization using decision tree ensemble and vibration data42
Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles42
Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents42
Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks41
Cable force estimation of a long‐span cable‐stayed bridge with microwave interferometric radar40
A deep reinforcement learning approach to mountain railway alignment optimization39
Shear loading detection of through bolts in bridge structures using a percussion‐based one‐dimensional memory‐augmented convolutional neural network39