Predicting Wall Thickness Loss in Water Pipes Using Machine Learning Techniques

被引:6
|
作者
Taiwo, Ridwan [1 ]
Ben Seghier, Mohamed El Amine [2 ]
Zayed, Tarek [1 ]
机构
[1] Hong Kong Polytech Univ, Hung Hom, Hong Kong, Peoples R China
[2] OsloMet Oslo Metropolitan Univ, Dept Civil Engn & Energy Technol, N-0167 Oslo, Norway
来源
EUROPEAN ASSOCIATION ON QUALITY CONTROL OF BRIDGES AND STRUCTURES, EUROSTRUCT 2023, VOL 6, ISS 5 | 2023年
关键词
Pipe wall thickness; Water pipe failure; Random Forest; Gradient boosting machine; SHAP; Wall thickness loss; Machine learning models; Prediction;
D O I
10.1002/cepa.2075
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Wall thickness loss in water pipes has been found to be positively correlated with water pipe failure. The reliability of water pipes reduces as their wall thickness loss increases. Although previous studies have investigated pipe failure modeling using historical failure data, however, indirect failure modeling via wall thickness loss is yet to be explored. Hence, this study develops machine learning (ML) models to predict wall thickness loss in water pipes. Random Forest (RF) and Gradient Boosting Machine (GBM) are chosen as the base models and are integrated with Bayesian Optimization (BO) algorithm for hyperparameters selection. The predictive models are evaluated using root mean square error (RMSE), mean absolute error (MEA), mean absolute percentage error (MAPE), and coefficient of determination (R-2). Based on the evaluation metrics, the hybrid models (i.e., RF+ BO and GBM+BO) outperformed the base models (RF and GBM), showing the importance of the systematic selection of hyperparameters. The best model (RF + BO) achieved an RMSE, MAE, MAPE, and R-2 value of 3.212, 2.494, 11.506, and 0.910, respectively. These metrics show the high predictive capability of the model, which can be used by water infrastructure management to predict wall thickness loss in water pipes.
引用
收藏
页码:1087 / 1092
页数:6
相关论文
共 50 条
  • [1] Explainable ensemble models for predicting wall thickness loss of water pipes
    Taiwo, Ridwan
    Yussif, Abdul-Mugis
    Ben Seghier, Mohamed El Amine
    Zayed, Tarek
    AIN SHAMS ENGINEERING JOURNAL, 2024, 15 (04)
  • [2] Water distribution pipe lifespans: Predicting when to repair the pipes in municipal water distribution networks using machine learning techniques
    Farajzadeh, Nacer
    Sadeghzadeh, Nima
    Jokar, Nastaran
    PLOS WATER, 2024, 3 (01):
  • [3] Predicting IRI Using Machine Learning Techniques
    Sharma, Ankit
    Sachdeva, S. N.
    Aggarwal, Praveen
    INTERNATIONAL JOURNAL OF PAVEMENT RESEARCH AND TECHNOLOGY, 2023, 16 (01) : 128 - 137
  • [4] Predicting IRI Using Machine Learning Techniques
    Ankit Sharma
    S. N. Sachdeva
    Praveen Aggarwal
    International Journal of Pavement Research and Technology, 2023, 16 : 128 - 137
  • [5] Predicting Diabetes Using Machine Learning Techniques
    Kirgil, Elif Nur Haner
    Erkal, Begum
    Ayyildiz, Tulin Ercelebi
    2022 INTERNATIONAL CONFERENCE ON THEORETICAL AND APPLIED COMPUTER SCIENCE AND ENGINEERING (ICTASCE), 2022, : 137 - 141
  • [6] Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques
    Mwaura, Anselim M.
    Liu, Yong-Kuo
    SCIENCE AND TECHNOLOGY OF NUCLEAR INSTALLATIONS, 2021, 2021
  • [7] Predicting performance of swimmers using machine learning techniques
    Guerra-Salcedo, Cesar M.
    Janek, Libor
    Perez-Ortega, Joaquin
    Pazos-Rangel, Rodolfo A.
    WMSCI 2005: 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol 3, 2005, : 146 - 148
  • [8] Predicting Driver Destination using Machine Learning Techniques
    Manasseh, Christian
    Sengupta, Raja
    2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC), 2013, : 142 - 147
  • [9] Predicting bank insolvencies using machine learning techniques
    Petropoulos, Anastasios
    Siakoulis, Vasilis
    Stavroulakis, Evangelos
    Vlachogiannakis, Nikolaos E.
    INTERNATIONAL JOURNAL OF FORECASTING, 2020, 36 (03) : 1092 - 1113
  • [10] Predicting Blood Donors Using Machine Learning Techniques
    Kauten, Christian
    Gupta, Ashish
    Qin, Xiao
    Richey, Glenn
    INFORMATION SYSTEMS FRONTIERS, 2022, 24 (05) : 1547 - 1562