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Cracks in concrete cause initial structural damage to civil infrastructures such as buildings, bridges, and highways, which in turn causes further damage and is thus regarded as a serious safety concern. Early detection of it can assist in preventing further damage and can enable safety in advance by avoiding any possible accident caused while using those infrastructures. Machine learning-based detection is gaining favor over time-consuming classical detection approaches that can only fulfill the objective of early detection. To identify concrete surface cracks from images, this research developed a transfer learning approach (TL) based on Convolutional Neural Networks (CNN). This work employs the transfer learning strategy by leveraging four existing deep learning (DL) models named VGG16, ResNet18, DenseNet161, and AlexNet with pre-trained (trained on ImageNet) weights. To validate the performance of each model, four performance indicators are used: accuracy, recall, precision, and F1-score. Using the publicly available CCIC dataset, the suggested technique on AlexNet outperforms existing models with a testing accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.90</mn><mo>%</mo></mrow></semantics></math></inline-formula>, precision of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.92</mn><mo>%</mo></mrow></semantics></math></inline-formula>, recall of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.80</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and F1-score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.86</mn><mo>%</mo></mrow></semantics></math></inline-formula> for crack class. Our approach is further validated by using an external dataset, BWCI, available on Kaggle. Using BWCI, models VGG16, ResNet18, DenseNet161, and AlexNet achieved the accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.90</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.60</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.80</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.90</mn><mo>%</mo></mrow></semantics></math></inline-formula> respectively. This proposed transfer learning-based method, which is based on the CNN method, is demonstrated to be more effective at detecting cracks in concrete structures and is also applicable to other detection tasks.