{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16497"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16497","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Metal culvert renewal prioritization framework development: A study for Saskatchewan Highways","abstract":"Saskatchewan comprises over 250,000 km (160,000 mi) of roads, the highest length of road surface compared to any other Canadian province. Along these roads, over 26,500 culverts have been installed for passaging water flow. Some of which have been installed over a century ago with no recorded installation dates. The failure of culverts, caused by a wide range of factors such as environmental, structural, or material failures, can result in sudden and catastrophic consequences, leading to injuries or loss of life. Additionally, damaged culverts can negatively impact water quality by causing erosion and scour, as well as impeding the passage of aquatic species between their habitats upstream and downstream. Thus, timely replacement of culverts can play a crucial role in minimizing such damages. This study evaluates three methods, namely, ordinal logistic regression, artificial neural network, and Fuzzy Inference System, for the prioritization of culverts for renewal. Specifically, the approach presented in this research is developed for metal pipe materials, which are extensively used in culvert installations. Using the condition of 1,000 metal culverts located along Saskatchewan highways, the three methods were employed to rank their condition and determine their renewal time. The evaluation of model performance was conducted using a range of established metrics including the area under the ROC (Receiver Operating characteristic) curve, percentage of correct predictions (PCP), confusion matrix, accuracy, precision, recall, and F1 score. The results of the study indicate that the artificial neural network optimized by genetic algorithm outperforms the other two methods, providing the most effective approach for culvert renewal prioritization. Keywords: Culvert renewal; Ordinal Logistic Regression; Artificial Neural Network; Fuzzy Inference System.","abstract_html":"Saskatchewan comprises over 250,000 km (160,000 mi) of roads, the highest length of road surface compared to any other Canadian province. Along these roads, over 26,500 culverts have been installed for passaging water flow. Some of which have been installed over a century ago with no recorded installation dates. The failure of culverts, caused by a wide range of factors such as environmental, structural, or material failures, can result in sudden and catastrophic consequences, leading to injuries or loss of life. Additionally, damaged culverts can negatively impact water quality by causing erosion and scour, as well as impeding the passage of aquatic species between their habitats upstream and downstream. Thus, timely replacement of culverts can play a crucial role in minimizing such damages. This study evaluates three methods, namely, ordinal logistic regression, artificial neural network, and Fuzzy Inference System, for the prioritization of culverts for renewal. Specifically, the approach presented in this research is developed for metal pipe materials, which are extensively used in culvert installations. Using the condition of 1,000 metal culverts located along Saskatchewan highways, the three methods were employed to rank their condition and determine their renewal time. The evaluation of model performance was conducted using a range of established metrics including the area under the ROC (Receiver Operating characteristic) curve, percentage of correct predictions (PCP), confusion matrix, accuracy, precision, recall, and F1 score. The results of the study indicate that the artificial neural network optimized by genetic algorithm outperforms the other two methods, providing the most effective approach for culvert renewal prioritization. Keywords: Culvert renewal; Ordinal Logistic Regression; Artificial Neural Network; Fuzzy Inference System.","abstract_has_math":false,"creators":["Marzbani, Pooneh"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Industrial Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Kabir, Golam"],"committee_chairs":[],"committee_members":["Khan, Sharfuddin","Khondoker, Mohammad"],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-24T04:03:43Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4701"],"render_values":[{"text":"https://doi.org/10.82465/4701","href":"https://doi.org/10.82465/4701","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16497","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kabir, Golam"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Khan, Sharfuddin","Khondoker, Mohammad"]},{"key":"dc:creator","label":"Author","values":["Marzbani, Pooneh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-11T21:13:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-11T21:13:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Industrial Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4701"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16497"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xii, 118 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Saskatchewan comprises over 250,000 km (160,000 mi) of roads, the highest length of road surface compared to any other Canadian province. Along these roads, over 26,500 culverts have been installed for passaging water flow. Some of which have been installed over a century ago with no recorded installation dates. The failure of culverts, caused by a wide range of factors such as environmental, structural, or material failures, can result in sudden and catastrophic consequences, leading to injuries or loss of life. Additionally, damaged culverts can negatively impact water quality by causing erosion and scour, as well as impeding the passage of aquatic species between their habitats upstream and downstream. Thus, timely replacement of culverts can play a crucial role in minimizing such damages. This study evaluates three methods, namely, ordinal logistic regression, artificial neural network, and Fuzzy Inference System, for the prioritization of culverts for renewal. Specifically, the approach presented in this research is developed for metal pipe materials, which are extensively used in culvert installations. Using the condition of 1,000 metal culverts located along Saskatchewan highways, the three methods were employed to rank their condition and determine their renewal time. The evaluation of model performance was conducted using a range of established metrics including the area under the ROC (Receiver Operating characteristic) curve, percentage of correct predictions (PCP), confusion matrix, accuracy, precision, recall, and F1 score. The results of the study indicate that the artificial neural network optimized by genetic algorithm outperforms the other two methods, providing the most effective approach for culvert renewal prioritization. Keywords: Culvert renewal; Ordinal Logistic Regression; Artificial Neural Network; Fuzzy Inference System."]},{"key":"dc:title","label":"Title","values":["Metal culvert renewal prioritization framework development: A study for Saskatchewan Highways"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kabir, Golam"],"dc:contributor.committeemember":["Khan, Sharfuddin","Khondoker, Mohammad"],"dc:creator":["Marzbani, Pooneh"],"dc:date.accessioned":["2024-10-11T21:13:06Z"],"dc:date.available":["2024-10-11T21:13:06Z"],"dc:date.issued":["2023-08"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xii, 118 p."],"dc:description.abstract":["Saskatchewan comprises over 250,000 km (160,000 mi) of roads, the highest length of road surface compared to any other Canadian province. Along these roads, over 26,500 culverts have been installed for passaging water flow. Some of which have been installed over a century ago with no recorded installation dates. The failure of culverts, caused by a wide range of factors such as environmental, structural, or material failures, can result in sudden and catastrophic consequences, leading to injuries or loss of life. Additionally, damaged culverts can negatively impact water quality by causing erosion and scour, as well as impeding the passage of aquatic species between their habitats upstream and downstream. Thus, timely replacement of culverts can play a crucial role in minimizing such damages. This study evaluates three methods, namely, ordinal logistic regression, artificial neural network, and Fuzzy Inference System, for the prioritization of culverts for renewal. Specifically, the approach presented in this research is developed for metal pipe materials, which are extensively used in culvert installations. Using the condition of 1,000 metal culverts located along Saskatchewan highways, the three methods were employed to rank their condition and determine their renewal time. The evaluation of model performance was conducted using a range of established metrics including the area under the ROC (Receiver Operating characteristic) curve, percentage of correct predictions (PCP), confusion matrix, accuracy, precision, recall, and F1 score. The results of the study indicate that the artificial neural network optimized by genetic algorithm outperforms the other two methods, providing the most effective approach for culvert renewal prioritization. Keywords: Culvert renewal; Ordinal Logistic Regression; Artificial Neural Network; Fuzzy Inference System."],"dc:identifier.doi":["https://doi.org/10.82465/4701"],"dc:identifier.uri":["https://hdl.handle.net/10294/16497"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Metal culvert renewal prioritization framework development: A study for Saskatchewan Highways"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Industrial Systems"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:43Z"}