OOIR: Observatory of International Research

Papers

(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.)

Article	Citations
Automated pavement crack detection and segmentation based on two‐step convolutional neural network	170
Deep learning for data anomaly detection and data compression of a long‐span suspension bridge	144
Generative adversarial network for road damage detection	133
Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system	115
Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine	105
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles	87
A deep‐learning‐based computer vision solution for construction vehicle detection	85
Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure	77
Uncertainty‐assisted deep vision structural health monitoring	76
Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption	73
Deep reinforcement learning for long‐term pavement maintenance planning	73
Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network	72
Cross‐scene pavement distress detection by a novel transfer learning framework	71
Real‐time regional seismic damage assessment framework based on long short‐term memory neural network	70
Noncontact cable force estimation with unmanned aerial vehicle and computer vision	68
Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning	61
Applicability of machine learning to a crack model in concrete bridges	57
Crack detection using fusion features‐based broad learning system and image processing	56
Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks	54
Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot	54
Vibration‐based semantic damage segmentation for large‐scale structural health monitoring	53
Efficient training of physics‐informed neural networks via importance sampling	52
Automatic railroad track components inspection using real‐time instance segmentation	52
Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold	51
Semi‐supervised learning based on convolutional neural network and uncertainty filter for façade defects classification	50

Hybrid deep learning architecture for rail surface segmentation and surface defect detection	50
Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network	48
Deep learning for post‐hurricane aerial damage assessment of buildings	47
Balanced semisupervised generative adversarial network for damage assessment from low‐data imbalanced‐class regime	46
Pavement defect detection with fully convolutional network and an uncertainty framework	45
Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras	43
Structural damage detection and localization using decision tree ensemble and vibration data	42
Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles	42
Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents	42
Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks	41
Cable force estimation of a long‐span cable‐stayed bridge with microwave interferometric radar	40
A deep reinforcement learning approach to mountain railway alignment optimization	39
Shear loading detection of through bolts in bridge structures using a percussion‐based one‐dimensional memory‐augmented convolutional neural network	39