This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research.

Moisture marks (wet areas) are significant defects that may develop on the surfaces of subway structures as a result of water leakage through soil. The detection and assessment of moisture marks are predominantly conducted on the basis of visual inspection (VI) methods, which are known to be costly, labor-intensive, and error-prone. The objective of this paper is to develop an integrated model based on image processing techniques and artificial intelligence to automate consistent moisture marks detection and numerical representation of the distress in subway networks. The integrated model comprises sequential processors that automatically detect moisture marks on concrete surfaces, and artificial neural networks (ANNs) for moisture marks identification and quantification. First, red-green-blue (RGB) images are preprocessed by means of spatial domain filters to denoise the image and enhance the crucial clues associated with moisture marks. Second, a moisture detector is streamlined with a set of morphological algorithms to detect wet areas. Third, the area percentage and severity of moisture marks are measured using the ANN model in conjunction with cross-entropy optimization function. The integrated model was validated through 165 images. Regarding the moisture marks detection algorithm, the recall, precision, and accuracy attained were 93.2, 96.1, and 91.5%, respectively. The mean and standard deviation of error percentage in moisture marks region extraction were 12.2 and 7.9%, respectively. Also, the ANN model was able to satisfactorily quantify the moisture marks area with an average validity of 96%. The integrated model is a decision support tool, expected to assist infrastructure managers and civil engineers in their future plans and decision making.