John C. Matthews

Sai Nethra Betgeri, Shashank Reddy Vadyala, John C. Matthews et al.

Risk-based assessment in pipe condition mainly focuses on prioritizing the most critical assets by evaluating the risk of pipe failure. This paper's goal is to classify a comprehensive pipe rating model which is obtained based on a series of pipe physical, external, and hydraulic characteristics that are identified for the proposed methodology. The traditional manual method of assessing sewage structural conditions takes a long time. By building an automated process using K-Nearest Neighbors (K-NN), this study presents an effective technique to automate the identification of the pipe defect rating using the pipe repair data. First, we performed the Shapiro Wilks Test for 1240 data from the Dept. of Engineering & Environmental Services, Shreveport, Louisiana Phase 3 with 12 variables to determine if factors could be incorporated in the final rating. We then developed a K-Nearest Neighbors model to classify the final rating from the statistically significant factors identified in Shapiro Wilks Test. This classification process allows recognizing the worst condition of wastewater pipes that need to be replaced immediately. This comprehensive model is built according to the industry-accepted and used guidelines to estimate the overall condition. Finally, for validation purposes, the proposed model is applied to a small portion of a US wastewater collection system in Shreveport, Louisiana. Keywords: Pipe rating, Shapiro Wilks Test, K-Nearest Neighbors (KNN), Failure, Risk analysis

Shashank Reddy Vadyala, Sai Nethra Betgeri1, John C. Matthews et al.

The recent development of machine learning (ML) and Deep Learning (DL) increases the opportunities in all the sectors. ML is a significant tool that can be applied across many disciplines, but its direct application to civil engineering problems can be challenging. ML for civil engineering applications that are simulated in the lab often fail in real-world tests. This is usually attributed to a data mismatch between the data used to train and test the ML model and the data it encounters in the real world, a phenomenon known as data shift. However, a physics-based ML model integrates data, partial differential equations (PDEs), and mathematical models to solve data shift problems. Physics-based ML models are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear equations. Physics-based ML, which takes center stage across many science disciplines, plays an important role in fluid dynamics, quantum mechanics, computational resources, and data storage. This paper reviews the history of physics-based ML and its application in civil engineering.