Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study

• URL: http://arxiv.org/abs/2502.00363v1
• Date: Sat, 01 Feb 2025 08:16:08 GMT
• Title: Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study
• Authors: Mohsen Mohammadagha, Mohammad Najafi, Vinayak Kaushal, Ahmad Mahmoud Ahmad Jibreen,
• Abstract summary: The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues. Around 75,000 yearly sanitary sewer overflows present serious economic, environmental, and public health hazards. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models.
• Score: 0.0
• License:
• Abstract: The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not account for the unpredictable nature of sewer decline, whereas probabilistic methods depend on extensive historical data, which is frequently lacking or incomplete. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models artificial neural networks (ANNs) and multiple linear regression (MLR) by integrating factors such as pipe age, material, diameter, environmental influences, and PACP ratings. ANNs utilized ReLU activation functions and Adam optimization, whereas MLR applied regularization to address multicollinearity, with both models assessed through metrics like RMSE, MAE, and R2. The findings indicated that ANNs surpassed MLR, attaining an R2 of 0.9066 compared to MLRs 0.8474, successfully modeling nonlinear relationships while preserving generalization. MLR, on the other hand, offered enhanced interpretability by pinpointing significant predictors such as residual buildup. As a result, pipeline degradation is driven by pipe length, age, and pipe diameter as key predictors, while depth, soil type, and segment show minimal influence in this analysis. Future studies ought to prioritize hybrid models that merge the accuracy of ANNs with the interpretability of MLR, incorporating advanced methods such as SHAP analysis and transfer learning to improve scalability in managing infrastructure and promoting environmental sustainability.

Related papers

• ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation [50.80115710105251]
  Event cameras hold significant promise for high-temporal-resolution (HTR) motion estimation. We propose a residual-based paradigm for estimating HTR optical flow with event data.
  arXiv  Detail & Related papers  (2024-12-12T09:35:47Z)
• Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater Contamination [0.0]
  This study expands the capabilities of PyLEnM, a Python package designed for long-term environmental monitoring. We introduce methods to estimate the timeframe required for contaminants like Sr-90 and I-129 to reach regulatory safety standards. Our methods are illustrated using data from the Savannah River Site (SRS) F-Area, where preliminary findings reveal a notable downward trend in contaminant levels.
  arXiv  Detail & Related papers  (2024-11-15T14:21:32Z)
• Semiparametric inference for impulse response functions using double/debiased machine learning [49.1574468325115]
  We introduce a machine learning estimator for the impulse response function (IRF) in settings where a time series of interest is subjected to multiple discrete treatments. The proposed estimator can rely on fully nonparametric relations between treatment and outcome variables, opening up the possibility to use flexible machine learning approaches to estimate IRFs.
  arXiv  Detail & Related papers  (2024-11-15T07:42:02Z)
• Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model [5.746772420339137]
  We develop an accurate model for the early prediction of pipeline leaks. This is the first application to use internal pipeline data for early prediction of leaks.
  arXiv  Detail & Related papers  (2024-11-09T15:34:38Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models [0.0]
  This study proposes a new methodology for predicting households' evacuation decisions constructed by easily accessible demographic and resource-related predictors. The proposed methodology could provide a new tool and framework for the emergency management authorities to improve the estimation of evacuation traffic demands.
  arXiv  Detail & Related papers  (2023-03-12T03:45:44Z)
• On Continual Model Refinement in Out-of-Distribution Data Streams [64.62569873799096]
  Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams. Existing continual learning (CL) problem setups cannot cover such a realistic and complex scenario. We propose a new CL problem formulation dubbed continual model refinement (CMR)
  arXiv  Detail & Related papers  (2022-05-04T11:54:44Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• A Locally Adaptive Interpretable Regression [7.4267694612331905]
  Linear regression is one of the most interpretable prediction models. In this work, we introduce a locally adaptive interpretable regression (LoAIR) Our model achieves comparable or better predictive performance than the other state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-05-07T09:26:14Z)
• Stream-Flow Forecasting of Small Rivers Based on LSTM [3.921808417990452]
  This paper tries to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model. We collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)
  arXiv  Detail & Related papers  (2020-01-16T07:14:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.