TY - JOUR
T1 - Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)
AU - Doğan, Neşet Berkay
AU - Ayhan, Bilal Umut
AU - Kazar, Gokhan
AU - Saygili, Murathan
AU - Ayözen, Yunus Emre
AU - Tokdemir, Onur Behzat
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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 Case-based 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.
AB - 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 Case-based 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.
KW - analytic hierarchy process
KW - case-based reasoning
KW - genetic algorithm
KW - predictive model
KW - quality problems
UR - http://www.scopus.com/inward/record.url?scp=85141860965&partnerID=8YFLogxK
U2 - 10.3390/buildings12111946
DO - 10.3390/buildings12111946
M3 - Article
AN - SCOPUS:85141860965
SN - 2075-5309
VL - 12
JO - Buildings
JF - Buildings
IS - 11
M1 - 1946
ER -