**6. Contribution to the Body of Knowledge**

Despite its importance, construction organizations are rarely aware of the rework impact on their budgets and on safe and environmental performances [34]. Small-sized construction companies still do not appreciate the magnitude of profit loss due to poor quality as they do not usually allocate CQM within a budget [13]. CQM and QC are not limited to error and violation prevention measures, but can also contribute to coping strategies, such as planning alternative countermeasures. The ability to predict COR allows for timely decision making for the required countermeasures, which can also improve the construction time, cost, and quality performance [2]. If a construction budget cannot sustain COR, it is hard to correct a construction error.

In this context, an ensemble COR predictor allows for dynamic and fast cost impact estimations throughout a project and offers more reliable cost impact estimations than the existing, single ML approaches. This is especially useful for cost variation and content analysis using Pareto and pie chart techniques. Throughout the construction lifecycle, the accumulation of project experiences adds to the knowledge of the ensemble models, which can further enhance their cost impact prediction accuracy and thus facilitate enhanced strategic planning by prioritizing the quality control items with the greatest cost impact. Therefore, the proposed COR impact estimator can enhance decision making and the associated planning for construction professionals. Specifically, relationship-style construction contracting models, such as alliance contracts, incorporate an element of error though procuring the construction project under a 'no blame, no fault' culture.

Generally, estimating COR improves cost, schedule, and resource-allocation planning, enhances the creation of the associated contingency plans [16], and also increases the visibility of the expected failure scenarios when purchasing the rework insurance, which is usually added to the general liability policy. This study has focused on estimating the impact of COR on overall construction cost, so the ability of ensemble construction cost predictors to improve the cost impact estimation in these areas remains to be addressed in future research. From a technical perspective, the ensemble method adopted needs to be further improved using different engineering features and optimization techniques to enhance the model prediction accuracy, especially for the underrepresented cost classes.

#### **7. Conclusions**

The early estimation of COR offers several benefits for construction professionals in terms of increasing the preparedness of the construction budget for dealing with risk (i.e., COR). On the one hand, the construction quality management literature is still limited with regard to the ML-based COR predictors, while on the other hand, the developed single ML predictors within the other cost estimation fields are not responsive for underrepresented classes with limited data records. However, a COR predictor does not solve this problem unless it can predict all the cost impact classes. Therefore, this study has proposed a robust ensemble ML predictor for estimating the cost of construction rework.

The adopted ensemble voting classifiers proved to be more effective in predicting the underrepresented high-cost impact construction rework activities than the benchmark models. Both single ML and tree-based predictors failed to estimate COR with (very) highcost impacts on overall construction budgets. Additionally, the soft voting classifier proved to be consistent in the accuracy of its prediction outcomes and was able to classify all the different COR impacts for three-, four-, and five-level classification tasks. The developed COR impact predictor increases the reliability and accuracy of the cost impact estimation, which, in turn, enables dynamic cost variation analysis and thus improves cost-based decision making.

COR has many undesirable effects, from cost fluctuations to the waste of material and labor and equipment hours. Ultimately, it is one of the crucial aspects of sustainability in construction. Thus, the early identification of high-cost impact rework items allows for a focus on countermeasures to prevent critical rework items. This in turn reduces the waste in construction flow, time, and material consumption while enhancing the different aspects of project performance, such as budget and quality performance. Finally, the discussed aspects of the project improve its overall sustainability level in terms of quality, economy, and waste criteria. Therefore, we recommend the further exploration of the use of different ML methods to predict and reduce COR.

**Author Contributions:** Conceptualization, V.T. and F.M.; formal analysis, V.T. and F.M.; writing—original draft preparation, V.T. and F.M.; writing—review and editing, V.T., F.M., Y.E.A. and O.B.T.; data curation, Y.E.A. and O.B.T.; resources, Y.E.A. and O.B.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

