Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)

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dc.authorid0000-0002-1000-1678en_US
dc.contributor.authorAyhan, Bilal Umut
dc.date.accessioned2022-12-14T10:42:27Z
dc.date.available2022-12-14T10:42:27Z
dc.date.issued2022-11-26
dc.description.abstractAbstract: Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study deals with non-conformities proactively by adopting an AI-based predictive model approach. The main objective of this study is to provide an automated solution structured on the data recording system for the adverse impacts of construction quality failures. For this purpose, we collected 2527 non-conformance reports from 59 diverse construction projects to develop a predictive model regarding the cost impact of the quality problems. The first of three stages forming the backbone of the study determines crucial attributes linked to quality problems through a literature survey and the Delphi method. Secondly, the Analytical Hierarchy Process (AHP) and a Genetic Algorithm (GA) were used to determine the attribute weights. In the final stage, we developed models to predict the cost impacts of non-conformities, using Casebased Reasoning (CBR). We made a comparison between the developed models to select the most precise one. The results show that the performance of CBR-GA using an automated weighting model is slightly better than CBR-AHP based on a subjective weighting system, whereas the case is the opposite in standard deviation in forecasting the cost outcome of the quality failures. Using both automated and expert systems, the study forecasts the cost impact of failures and reveals the factors linked to poor record-keeping. Ultimately, we concluded that the outcome of non-conformities can be predicted and prevented using past events via the developed AI-based predictive model.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.3390/buildings12111946en_US
dc.identifier.issn2075-5309
dc.identifier.scopus2-s2.0-85141860965en_US
dc.identifier.urihttps://hdl.handle.net/11411/4750
dc.identifier.urihttps://doi.org/10.3390/buildings12111946
dc.identifier.wosWOS:000884504200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue1en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors5en_US
dc.publisherMDPIen_US
dc.relation.ispartofBuildingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectpredictive modelen_US
dc.subjectcase-based reasoningen_US
dc.subjectanalytic hierarchy processen_US
dc.subjectgenetic algorithmen_US
dc.subjectquality problemsen_US
dc.titlePredicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)en_US
dc.typeArticleen_US
dc.volume12en_US

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