**1. Introduction**

A successful construction project is delivered on time and within budget, conforming to the specified quality. To achieve this, potential construction errors and violations are managed by applying an adequate construction quality management (CQM) system. An indispensable procedure within CQM is quality control (QC), which involves ensuring construction activity delivery at a specified standard, appraising its conformance, and maintaining continuous quality improvement. In construction projects, the arrays of errors, omissions, negligence, changes, failures, and violations resulting from poor management, communication, and coordination, or the materialization of potential risks are solved through rework. Thus, it is necessary to put in place a construction QC mechanism that not only prevents the need for rework but also prepares for accepting, acting on, and coping with required rework. Hence, the cost of rework (COR) is an inseparable component of overall construction costs, and its reduction directly improves construction cost and quality performance.

Although construction rework has been addressed in the literature, it remains a widespread [1] and prevalent problem [2,3], and poses a real challenge [4–6]. Despite all the advances in philosophies such as lean and total quality management (TQM) in preventing construction errors, COR still accounts for a considerable portion of the total project cost [2,7–9] and affects the construction schedule and quality [10]. Construction rework directly impacts the contract value by 5% to 20% [2], which can lead to complete project failure. Measuring COR enables the CQM system to control the construction budget and improve cost performance while allowing construction professionals to better understand the magnitude of the rework, its causes, and decisions on rework prevention measures [9]. Identifying the impact of COR and its sources enables reductions in the amount of rework

**Citation:** Mostofi, F.; To ˘gan, V.; Ayözen, Y.E.; Behzat Tokdemir, O. Predicting the Impact of Construction Rework Cost Using an Ensemble Classifier. *Sustainability* **2022**, *14*, 14800. https://doi.org/10.3390/ su142214800

Academic Editors: Albert P. C. Chan, Srinath Perera, Xiaohua Jin, Patrizia Lombardi, Dilanthi Amaratunga, Anil Sawhney, Makarand Hastak and Sepani Senaratne

Received: 29 September 2022 Accepted: 7 November 2022 Published: 9 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and improvements in construction cost performance [11]. It is noteworthy that anticipating COR facilitates the utilization of QC techniques, such as Pareto analysis and pie charts. These QC techniques are dynamically used throughout the construction lifecycle to predict the construction rework items with a high-cost impact, which, in turn, allows for the timely adjustment of the associated construction schedule, budget, quality, human resources, and communication plans for the appropriate countermeasures. It is also noteworthy that obtaining COR is a key to understanding the cost of quality (COQ), i.e., the conformance costs, and the nonconformance costs, also referred to as cost of poor quality (COPQ) [12]. The ability of construction firms to measure COQ is essential for their survival in today's competitive environment [13].

While the construction management literature agrees on the important contribution of COR to total construction cost, it is not consistent with respect of the magnitude of COR's impact on overall construction cost, estimates of which vary between as much as 0.5% and 20% of the overall contract value [14]. This range reduces the practicality of COR in implementing effective countermeasures during the stages of a project. Additionally, in practice, countermeasures should focus on the construction work items with the higher impact on the total construction cost. It is not always feasible to implement preventive countermeasures or rework management strategies for all rework items associated with different construction activities. Evaluating the cost impact of construction rework supports early decision making on high-impact nonconformance items. For example, ranking the most impactful building defects offers construction companies insight for selecting the most appropriate strategy to continuously improve their construction activities and in turn to support sustainable decision making for the design and operation of buildings [15]. Furthermore, unless the COR for each construction activity is measured, it cannot be compared with the cost of the associated prevention or control plan. COR influences the construction budget, risk, and quality plans, which, in turn, affects the decision making associated with other project management knowledge areas. In order to improve the CQM, budget, and schedule plan, therefore, it is necessary to estimate the COR for each construction activity. This increases the error preparedness of construction organizations, which enhances decision-making resilience and plan accuracy while facilitating the prompt implementation of appropriate countermeasures, and thus also allows for appropriate contingency plans to be developed while helping the manager prevent later issues in other construction project phases [16].

In this study, COR is predicted for the total construction cost, which is a critical decision-making parameter. Experiments with advanced machine learning (ML) models, such as the ensemble method for predicting construction cost and COR, are lacking in the literature. Therefore, our work uses ensemble learning applied to the widely used construction nonconformity reports (NCRs) to ensure the robustness of the created COR predictor. As outlined in Figure 1, the main objective of this study is to assist construction quality managers and cost managers in including COR in their evaluations of different construction activities.

The remainder of this study is organized as follows. The next section discusses construction rework and its impact on overall construction cost. In addition, ensemble learning as a subdiscipline of ML is introduced, and, since the literature is limited to MLbased COR estimators, the ML applications for CQM and cost estimation are reviewed. The following section describes the NCRs obtained from different construction projects in Turkey. Then, the adopted methodology and ensemble COR predictor details are presented, and the benchmark ML predictor configuration is outlined. The next section gives the results that were obtained and discusses the practical implementations of the COR predictor that was developed and its contribution to the existing research on construction management. Finally, a brief conclusion reviews the study findings.

**Figure 1.** Study outline.
