Yazar "Ayhan, Bilal Umut" seçeneğine göre listele

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    Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Doğan, Neşet Berkay; Ayhan, Bilal Umut; Kazar, Gökhan; Saygılı, Murathan; Ayözen, Yunus Emre; Tokdemir, Onur Behzat
    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.
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    Öğe
    Quality Failures-Based Critical Cost Impact Factors: Logistic Regression Analysis
    (Asce-Amer Soc Civil Engineers, 2022) Kazar, Gökhan; Doğan, Neset Berkay; Ayhan, Bilal Umut; Tokdemir, Onur Behzat
    Due to the various dynamic conditions of construction sites, quality failures have become part and parcel of the industry. Many studies have identified the causal factors of construction site quality failures and their cost impacts. However, limited studies have been made evaluating the domino effects of these on one another and the correlation between the cost impact and frequency of each attribute. In this study, made in the context of ongoing research into related artificial intelligence (AI)-based predictive models, a total of 2,527 nonconformance reports (NCRs) collected from 59 construction projects within the scope of a previous study were analyzed using the Delphi method and logistic regression analysis. According to the Delphi results, 25 critical cost impact factors were refined and categorized into five main groups: Materials, Design, Installation, Operation, and Process. Then, five main hypotheses were developed to test each attribute's cost impact and interaction by logistic regression. The results showed that although some attributes (from the Materials and Operation groups) have a significant impact on the cost of quality if observed in a failure report individually, others may become a critical cost-impact factor when interacting with other attributes. No significant correlation was observed between the frequency and cost impact of the attributes. Finally, a holistically based quality control system that considers the domino effects of causal factors from planning to operation was proposed for construction practitioners to reduce quality failures causing cost and time overruns.