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Öğe Data-driven ensemble learning approach for optimal design of cantilever soldier pile retaining walls(Elsevier, 2023) Çakıroğlu, Celal; Islam, Kamrul; Bektaş, Gebrail; Nehdi, Moncef L.Cantilever soldier pile retaining walls are used to ensure the stability of excavations. This paper deploys ensemble machine learning algorithms towards achieving optimum design of these structures. A large dataset was developed consisting of 40,569 combinations of pile geometry, external loading, soil properties, and concrete unit cost, with two different values of soil reaction coefficient. Optimum pile diameter that minimizes the total cost of the retaining wall was computed considering the structural load-carrying capacity as the optimization constraint. The dataset was split into training and testing sets at 70% to 30% ratio. The predictive accuracy of the ensemble machine learning models was appraised on the testing dataset using various statistical metrics. Model performance was also evaluated for its ability in predicting the optimum pile diameter. The developed models demonstrated excellent predictive accuracy. Furthermore, the effect of different input variables on the model predictions was explained using the SHapely Additive exPlanations (SHAP) approach. Through the SHAP algorithm, the pile length was identified as the design variable having the most significant effect on the optimum pile diameter. The study demonstrates ensemble learning techniques as a viable alternative to the traditional techniques in the optimum design of cantilever soldier pile retaining walls.Öğe Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete(Elsevier, 2024) Çakıroğlu, Celal; Tusher, Tanvir Hassan; Shahjalal, Md.; Islam, Kamrul; Billah, A. H. M. Muntasir; Nehdi, Moncef L.Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland’s algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.Öğe Explainable ensemble learning predictive model for thermal conductivity of cement-based foam(Elsevier Sci Ltd, 2024) Cakiroglu, Celal; Batool, Farnaz; Islam, Kamrul; Nehdi, Moncef L.Cement-based foam has emerged as a strong contender in sustainable construction owing to its superior thermal and sound insulation properties, fire resistance, and cost-effectiveness. To effectively use cement-based foam as a thermal insulation material, it is important to accurately predict its thermal conductivity. The current study aims at coining an accurate methodology for predicting the thermal conductivity of cement-based foam using state-ofthe-art machine learning techniques. A comprehensive experimental dataset of 504 data points was developed and used for training ensemble learning models including XGBoost, CatBoost, LightGBM and Random Forest. The independent variables of this dataset affecting the thermal conductivity are the cast density, percentage of pozzolan, porosity, percentage of moisture, and duration of hydration in days. Using the Isolation Forest algorithm proved effective in detecting and eliminating outliers in the dataset. All the ensemble learning techniques explored in this study achieved superior predictive accuracy with a coefficient of determination greater than 0.98 on the test dataset. The influence of the input features on the thermal conductivity was visualized using the SHapley Additive exPlanations (SHAP) approach and individual conditional expectation (ICE) plots. The cast density had the greatest effect on thermal conductivity. The explainable machine learning models demonstrated superior accuracy, efficiency, and reliability in estimating the thermal insulation of cement-based foam, opening the door for wider acceptance of this material in sustainable energy efficient construction.Öğe Explainable machine learning model for predicting punching shear strength of FRC flat slabs(2024) Liu, Tongxu; Çakıroğlu, Celal; Islam, Kamrul; Wang, Zhen; Nehdi, Moncef L.Reinforced concrete slabs are vulnerable to punching shear failure at the slab-column joint, which can initiate catastrophic progressive collapse. The addition of steel fibers in the concrete matrix has emerged as an effective strategy to mitigate such progressive failure. However, the effects of the diverse mixture proportions of the concrete matrix with different types and dosages of fibers have made the accurate prediction of the punching shear strength (PSS) of the fiber-reinforced concrete (FRC) flat slabs a complex task, where the existing me chanical models have several limitations. Therefore, this study proposes an explainable XGBoost model for predicting PSS of flat slabs made with different types of FRC based on a newly established comprehensive database of 251 flat slabs including normal strength FRC slabs, high-performance FRC slabs, and ultra-highperformance FRC slabs. A customized procedure was proposed to establish the XGBoost model considering data preparation, feature selection, hyperparameter tuning and model validation. The performance of the XGBoost model was then compared with that of existing mechanical models. Finally, sensitivity analysis and SHapley Additive exPlanations (SHAP) analysis were applied to identify the most influential parameters on the prediction of PSS. Results show that the proposed feature selection method is effective in identifying six influ ential parameters from the eleven parameters related to the PSS of FRC flat slabs. The developed XGBoost model yielded highest prediction accuracy and lowest variation, which outperformed the other mechanical models. Sensitivity analysis also indicated similar trends of parameters in both the XGBoost model and the mechanical models. The PSS of FRC flat slabs can be improved by increasing the concrete compressive strength, reinforce ment ratio, and fiber volume, and by decreasing the column width-to-depth ratio, water-to-binder ratio, and aggregate size ratio. The proposed XGBoost model could enhance the understanding of PSS of FRC flat slabs and guide future pertinent design code provisions.
