Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for punching strength of FRP reinforced concrete slabs have some problems such as high bias and variance. This study established machine learning-based models to accurately predict the punching shear strength of FRP reinforced concrete slabs. A database of 121 groups of experimental results of FRP reinforced concrete slabs are collected from a literature review. Several machine learning algorithms, such as artificial neural network, support vector machine, decision tree, and adaptive boosting, are selected to build models and compare the performance between them. To demonstrate the predicted accuracy of machine learning, this paper also introduces 6 empirical models and design codes for comparative analysis. The comparative results demonstrate that adaptive boosting has the highest predicted precision, in which the root mean squared error, mean absolute error and coefficient of determination of which are 29.83, 23.00 and 0.99, respectively. GB 50010-2010 (2015) has the best predicted performance among these empirical models and design codes, and ACI 318-19 has the similar result. In addition, among these empirical models, the model proposed by El-Ghandour et al. (1999) has the highest predicted accuracy. According to the results obtained above, SHapley Additive exPlanation (SHAP) is adopted to illustrate the predicted process of AdaBoost. SHAP not only provides global and individual interpretations, but also carries out feature dependency analysis for each input variable. The interpretation results of the model reflect the importance and contribution of the factors that influence the punching shear strength in the machine learning model.

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Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and R2 of the training set are 0.926, 1.596, 2.155 and 0.950 and the a10-index, MAE, RMSE and R2 of the test set are 0.659, 3.182, 4.285 and 0.842, respectively. SHapley Additive exPlanation (SHAP) is adopted to interpret the prediction process of XGBoost and explain the influence of influencing factors on the compressive strength of RP mortar. According to the importance of influencing factors, the order is the mass replacement rate of RP, the size of RP, the kind of RP and the water binder ratio of RP. The compressive strength of RP mortar decreases with the increase in the RP mass replacement rate. The compressive strength of RBP mortar is slightly higher than that of RCP mortar. Machine learning technologies will benefit the construction industry by facilitating the rapid and cost-effective evaluation of RP material properties.

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Reinforced concrete slab-column structures, despite their advantages such as architectural flexibility and easy construction, are susceptible to punching shear failure. In addition, punching shear failure is a typical brittle failure, which introduces difficulties in assessing the functionality and failure probability of slab-column structures. Therefore, the prediction of punching shear resistance and corresponding reliability analysis are critical issues in the design of reinforced RC slab-column structures. In order to enhance the computational efficiency of the reliability analysis of reinforced concrete (RC) slab-column joints, a database containing 610 experimental data is used for machine learning (ML) modelling. According to the nonlinear mapping between the selected seven input variables and the punching shear resistance of slab-column joints, four ML models, such as artificial neural network (ANN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) are established. With the assistance of three performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), XGBoost is selected as the best prediction model; its RMSE, MAE, and R2 are 32.43, 19.51, and 0.99, respectively. Such advantages are also reflected in the comparison with the five empirical models introduced in this paper. The prediction process of XGBoost is visualized by SHapley Additive exPlanation (SHAP); the importance sorting and feature dependency plots of the input variables explain the prediction process globally. Furthermore, this paper adopts Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of slab-column joints in a real engineering example. A total of 1,000,000 samples were obtained through random sampling, and the reliability index β of this practical building was calculated by Monte Carlo simulation. Results demonstrate that the target reliability index requirements under design provisions can be achieved. The sensitivity analysis of stochastic variables was then conducted, and the impact of that analysis on structural reliability was deeply examined.