[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127300-en":3,"doc-seo-127300-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127300,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","Prediction of Sewage Pipeline Construction Duration by Introducing Machine Learning and Deep Learning Approaches","Establishing project costs in construction is crucial for project success and is commonly supported by regression-based prediction methods. This study evaluates traditional and modern regression techniques using data from 83 sewage pipeline projects in South Korea. State-of-the-art frameworks with hyperparameter optimization and k-fold cross-validation measure statistical, machine learning, and deep learning models via R2, RMSE, MAE, and MSE. Findings show that performance metrics may not consistently match predictive accuracy, with polynomial regression achieving the highest validation accuracy.","ISSN 1392-3730 /eISSN 1822-3605  \nJOURNAL of CIVIL ENGINEERING and MANAGEMENT  \n2025 Volume 31 Issue 7  \nPages 687–709 [https://doi.org/10.3846/jcem.2025.23472](https://doi.org/10.3846/jcem.2025.23472)  \nPREDICTION OF SEWAGE PIPELINE CONSTRUCTION DURATION BY INTRODUCING MACHINE LEARNING AND DEEP LEARNING APPROACHES  \nSang-Jun PARK 1, Norhane NOUR 1, Kang Young LEE2, Ju-Hyung KIM 1  \n1 Department of Architectural Engineering, Hanyang University, Seoul, South Korea  \n2 SAMAN Engineering, Waterworks & Sewage Division 1, Seoul, South Korea  \n\n| Article History:\u003Cbr>■ received 24 October 2023\u003Cbr>■ accepted 27 September 2024 | Abstract. Establishing project costs in construction is crucial for project success, typically done through regression methods for prediction. While these methods are common, novel regression methods are less practiced in construction management. This study explores both traditional and modern regression techniques, analyzing data from 83 sewage pipeline projects in South Korea. The study implemented state-of-the-art frameworks, including hyperparameter optimization and k-fold cross-validation, to evaluate statistic, machine learning and deep learning based regression models using R2 score, RMSE, MAE, and MSE. Results revealed that performance metrics don’t always align with predictive accuracy. For instance, the random forest regressor achieved the best R2 score of 0.847 but ranked fifth in prediction accuracy. Moreover, polynomial regression outperformed novel methods with a 98.790% accuracy across the validation dataset. |  |\n| --- | --- | --- |\n| Keywords: construction management, sewage pipeline construction, statistical regression, machine learning regression, deep learning regression. |  |  |\n| Corresponding author. E-mail: [kcr97jhk@hanyang.ac.kr](kcr97jhk@hanyang.ac.kr) |  |  |\n| 1. Introduction |  |  |\n| Civil infrastructure is an indispensable element of all built |  | 54.7% of the total road damage cases (Kim, 2022) . Moreo- |\n| urban environments as it enables a wide range of human |  | ver, according to the Ministry of Land, Infrastructure and |\n| activities and provide public services such as transpor- |  | Transport of South Korea, a total of 8,424 km of water |\n| tation, water supply, sewage, gas, electricity, and power |  | and sewage pipes were installed for more than 40 years |\n| (Doyle & Havlick, 2009) . Out of the numerous civil in- |  | ago while 26,350 km were installed for 30 to 40 years. |\n| frastructures providing services to the residents, sewage |  | Therefore, there is a need to repair and replace these aged |\n| system is an essential service for modern living that can |  | sewage pipes. In 2018, Urban Infrastructure Headquarters |\n| impact the environment significantly. Sewage pipelines are |  | announced a management plan for old sewage pipes of |\n| considered one of the most crucial components of an ur- |  | 5,000 km by 2021 including a plan for strengthening 73% |\n| ban infrastructure system as they preserve public health by |  | of the old sewage pipes older than 20 years (Kim, 2022) . |\n| draining wastewater from densely populated areas to nec- |  | Moreover, according to the Ministry of Environment Do- |\n| essary treatment plants (Malek Mohammadi et al., 2019; Obradović, 2017; Opila, 2011) . It is considered as a large |  | mestic Sewage Division (2021), a plan in motion was announced in 2020 to plan for a large-scale construction pro- |\n| infrastructure typically constructed beneath roadways as |  | ject, up to 33,861,387 m of new pipes nationwide, which |\n| shown in Figure 1. Consequently, any damage that may |  | adds to the 163,098,677 m of existing sewage pipelines. |\n| occur to pipelines, such as pipe breakage or deteriora- |  | From the perspective of project managers for sewage |\n| tion, could likely cause damage to roads (Obradović et al., 2023) . In Seoul, between 2016 to 2021, a total of 1,431 |  | pipeline construction projects, risks, such as cost overruns, rela","cbCaioLVrnwNbMKe","https://ap.wps.com/l/cbCaioLVrnwNbMKe","pdf",4694099,1,23,"English","en",105,"# Article History\n## Received and Accepted Dates\n# Abstract\n# Keywords\n# 1. Introduction\n## Importance of Civil Infrastructure and Sewage Pipelines\n## Need for Repair and Replacement of Aging Pipes\n## Schedule Delays and Their Impacts","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To predict sewage pipeline construction duration using both traditional and modern regression approaches, including statistical, machine learning, and deep learning models.\"},{\"question\":\"Which data and evaluation strategy are used?\",\"answer\":\"The study uses 83 sewage pipeline projects in South Korea and applies hyperparameter optimization with k-fold cross-validation, evaluating models using R2, RMSE, MAE, and MSE.\"},{\"question\":\"Do the study’s metrics always reflect predictive accuracy?\",\"answer\":\"No. The results show performance metrics do not always align with predictive accuracy; for example, a model with the best R2 ranking may not be the top predictor by accuracy.\"}]","Prediction of Sewage Pipeline Construction Duration by Introducing Machine Learning and Deep Learning Approaches | PDF",1785938185,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-of-sewage-pipeline-construction-duration-by-introducing-machine-learning-and-deep-learning-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-sewage-pipeline-construction-duration-by-introducing-machine-learning-and-deep-learning-approaches/127300/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of this study?","Question",{"text":76,"@type":77},"To predict sewage pipeline construction duration using both traditional and modern regression approaches, including statistical, machine learning, and deep learning models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data and evaluation strategy are used?",{"text":81,"@type":77},"The study uses 83 sewage pipeline projects in South Korea and applies hyperparameter optimization with k-fold cross-validation, evaluating models using R2, RMSE, MAE, and MSE.",{"name":83,"@type":74,"acceptedAnswer":84},"Do the study’s metrics always reflect predictive accuracy?",{"text":85,"@type":77},"No. The results show performance metrics do not always align with predictive accuracy; 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