TL;DR
The proposed framework provides accurate, robust and interpretable prediction of rock mechanical properties, demonstrating its potential for geotechnical characterization and transportation infrastructure applications.
The results showed that all models successfully captured the relationship between the input parameters and uniaxial compressive strength, although their predictive capabilities differed considerably, and KStar produced the most accurate predictions.
This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data and reveals that neutron porosity, formation bulk density and Gamma Ray are the three most influential predictors.
Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting the shear strength of RC beams using a systematically curated experimental database comprising 1,977 specimens collected from published literature. The database was preprocessed to remove incomplete and duplicate records, and the optimal ANN architecture was selected using the total goodness function. Model performance was evaluated using 10-fold cross-validation together with multiple statistical metrics, including the coefficient of determination (R²), mean absolute error, root mean squared error, bias, and prediction interval. The ANN achieved R² values of 0.9968 and 0.9686 for the representative training and testing datasets, respectively, and an overall R² of 0.993, with 1,668 predictions falling within the ± 30% error criterion. Comparative evaluation with the Canadian Standards Association (CSA), American Concrete Institute 318 (ACI 318), and Eurocode 2 (EC2) design-code models demonstrated that the proposed ANN consistently achieved superior predictive accuracy and reliability. Parametric analyses further confirmed that the predicted trends agree with established reinforced concrete shear mechanics, highlighting the dominant influence of the shear span-to-depth ratio, beam geometry, and reinforcement ratio on shear resistance. The proposed framework provides an accurate and robust decision-support tool that complements conventional design-code methods for predicting the shear strength of RC beams.
This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations representing traffic demand, structural thickness, subgrade Poisson’s ratio, and soil properties. Candidate equations were generated through a hybrid evolutionary search combining Genetic Algorithm and Differential Evolution, and final models were selected by jointly considering statistical performance, parsimony, and Monte Carlo sensitivity consistency. Six predictors were retained: number of axle-load repetitions, percentage passing the No. 200 sieve, optimum moisture content, clayeyness coefficient, laterization index, and equivalent pavement thickness. Model assessment used three repeated random train-test partitions. The selected equations contained three polynomial terms and achieved testing coefficient-of-determination values from 0.943 to 0.951, root mean square errors from 0.214 to 0.238 mm, and mean absolute errors from 0.163 to 0.169 mm. Sensitivity and Shapley analyses showed physically consistent trends, including increased deformation with traffic loading and laterization index, and reduced deformation with equivalent pavement thickness.
Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.