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Jul 2026

An investigation of flexural resistance of high-performance steel fiber-reinforced cementitious composites: data-driven prediction and experimental verification

ABSTRACT This study establishes four popular data-driven techniques – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) for predicting the flexural strength (FS) of high-performance steel fiber-reinforced cementitious composites (FRCCs). A database containing 156 experimental records was used to train and test predictive models with nine feasible input variables. The results demonstrated that the GB model was the best predictor for estimating the FS of FRCCs. The GB model maintained satisfactory predictive accuracy after 10-fold cross-validation, achieving an average R2 of 0.834 on the validation folds generated from the training dataset. The predictive capability of GB model remained stable at 600 Monte Carlo simulations. The Shapley Additive Explanations (SHAP) method and partial dependence plots (PDP) indicated that fiber volume content was the most influential factor affecting FS predictions. To validate the accuracy of the GB model, a single-point case study was conducted on three specimens subjected to a three-point bending load. The discrepancy between the experimental values and GB predictions corresponded to an absolute prediction error of 1.21%, highlighting the accuracy of the developed model. Finally, a cloud-based web application was developed to provide a convenient tool for practical FS prediction of FRCCs.

Duy-Liem Nguyen, Tan-Duy Phan · 0 citations
2026

Machine learning-based prediction of pore characteristics in laser powder bed fusion-fabricated 316L stainless steel

Precise prediction and control of porosity in laser powder bed fusion (L-PBF) directly enhances the performance of additively manufactured components. This study addresses the need for comprehensive machine learning (ML) analysis of pore characteristics through comparative evaluation of multiple ML models based on accuracy and reliability. Five supervised ML models-linear regression (LR), Gaussian process regression, decision tree regression, artificial neural networks (ANNs), and random forest regression (RFR)-were utilized to predict pore characteristics in 316L stainless steel components fabricated via L-PBF. Model performance was systematically assessed using three key metrics: root mean square error, mean absolute error, and coefficient of determination ( R 2 ). These metrics were calculated from process-condition averages under grouped cross-validation to ensure robust evaluation. Material characterization of specimens produced using pore-optimized printing parameters further validated the predictive accuracy of the models. Our results revealed that distinct models were optimal for different pore-related targets: LR outperformed others for porosity prediction, RFR excelled in estimating average pore diameter, and ANN delivered the highest accuracy for average pore roundness. Response surfaces generated from the optimal models delineated a processing window (laser power: 150–250 W; scan speed: 800–1200 mm/s; layer thickness: 0.04 mm; and hatch spacing: 0.09 mm) associated with minimized porosity and improved pore morphology. Notably, a significant inverse correlation was observed between predicted porosity and critical mechanical properties (including yield strength, tensile strength, and elongation at break), which further corroborated the practical utility of the proposed predictive workflow.

Shi-yu Liu, Cheng Liu, Xiao Xue et al. · 0 citations
Conference Jul 2026

Prediction of compressive and flexural strength of modified concrete using machine learning

Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.

Yuchen Lin · 0 citations
Conference Open access Jul 2026

Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete

Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.

R. R. Khasani, Ferry Hermawan, Yuliana Usman · 0 citations
Open access Jul 2026

Machine Learning Prediction of Concrete Compressive Strength: Model Comparison, CatBoost Optimization, and SHAP Interpretation

A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies.

Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati · 0 citations
Review Open access Jul 2026

Machine Learning-Assisted Prediction and Multi-Objective Optimisation of Rice Husk Ash and Micro Fine Slag Stabilised Black Cotton Soil for Sustainable Pavement Subgrades

Black cotton soil is a highly expansive geomaterial whose high plasticity, low soaked bearing capacity and moisture-driven volume change frequently cause pavement distress, subgrade deformation and serviceability loss. Sustainable soil stabilisation using rice husk ash (RHA) and Micro Fine Slag (MFS) is promising because it can combine agro-industrial residue utilisation with mechanical improvement of problematic expansive soils. This manuscript presents an integrated civil engineering and artificial intelligence/machine learning (AI/ML) framework for predicting and optimising the performance of RHA-MFS stabilised black cotton soil. The study is written as a validation-oriented predictive screening manuscript: modelled and augmented data are clearly separated from direct laboratory evidence, and final design adoption is made conditional on confirmatory experimental testing. Input features include RHA content, MFS content, curing age, liquid limit, plastic limit, plasticity index, maximum dry density, optimum moisture content, specific gravity and pH. Target responses include soaked California Bearing Ratio (CBR), unconfined compressive strength (UCS) and free swell index (FSI). Linear regression, ridge regression, random forest, Extra Trees, gradient boosting and support vector regression with radial basis function kernel (SVR-RBF) are benchmarked. Model assessment includes R2, RMSE, MAE, MAPE, cross-validation, residual diagnostics, feature importance, SHAP-style interpretation and prediction-uncertainty logic. The Results and Discussion section integrates civil engineering graphs and AI/ML diagnostic figures directly within the main text. The screening results support a practical validation domain of 10-15% RHA and 6-9% MFS at 28-56 days of curing, with 10% RHA + 6% MFS proposed as the first confirmatory laboratory candidate. The contribution of the paper is a reviewer-defensible methodology that uses AI/ML to reduce experimental search space while retaining geotechnical mechanism, material characterisation, sustainability reasoning and transparent limits on model-based claims.

Nikita Rahaja, Ashok Kumar Gupta, Kushal Kanwar · 0 citations