Abstract
Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up‐to‐date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open‐source CNN models (Model1, Model2, and Model3) which have been well‐illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227‐pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.
Highlights
A significant number of civil infrastructures have progressively approached their life span expectancy; the integrity of the structural system needs to be checked
Among the 570,000 bridges in the USA, 40% were listed as deficient, requiring repair or reconstruction according to the requirements of the Federal Highway Administration (FHWA), with an expected cost of 50 billion dollars [8, 9]
The performances of three convolutional neural network (CNN) models have been compared using the same dataset for concrete crack detection. e models were trained for 10 epochs on Google Colab, which is a cloud computing platform for machine learning where accuracy and the categorical cross-entropy loss function are used to measure the performance of classification models
Summary
A significant number of civil infrastructures have progressively approached their life span expectancy; the integrity of the structural system needs to be checked. How to constantly and automatically check the structure with even less workforce has become a vital research path with the aging of the population and the rise in labor costs [1,2,3,4]. Because cracks will effectively function as a significant predictor in assessing structural damage, crack identification would have elevated functional implementation values in activating early bridge repair alerts, safety assurance, and loss mitigation [10]. The human-based visual inspection and evaluation are time-consuming and subjective [11]. The human-based visual inspection and evaluation are time-consuming and subjective [11]. e accuracy of damage diagnosis depends mainly on the skill level and experience of the inspectors. erefore, automatic damage detection is a crucial task for achieving objectivity and efficiency of damage assessment [12]
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