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Go to Editorial ManagerComposite civil engineering structural systems comprising steel sections and reactive powder concrete (RPC) have been receiving greet attention over the past few years as a result of their excellent structural efficiency, long-term durability, and sustainability. Compared to normal concrete, the characteristics of RPC, represented by its ultra-high performance in compression and tension, possess superior improvement in the structural industry. This review systematically presents recent advancements in three primary systems: (1) Reactive powder concrete, focusing on its composition, mixing procedure and efforts made to adopt normal curing method, (2) Composite structures in general, and (3) Composite steel and RPC structures. Special attention is devoted to the last structural system, including the degree of connection provided by the shear connectors and the structural performance of composite RPC and steel sections.
Accurate prediction of concrete compressive strength remains a critical challenge in structural engineering due to the complex, non-linear relationships between mixture components and mechanical properties. This study presents a comprehensive machine learning framework for predicting concrete compressive strength, incorporating both deterministic and probabilistic approaches. We analyzed 1030 laboratory-tested concrete specimens with eight input variables to evaluate four distinct algorithms: linear regression, support vector regression, random forest, and artificial neural networks. Additionally, we developed a Monte Carlo dropout neural network to quantify prediction uncertainty through Bayesian approximation. Results demonstrate that ensemble tree-based methods achieved superior predictive performance with a root mean square error of 5.47 MPa and coefficient of determination of 0.884, significantly outperforming conventional approaches at the 0.01 significance level. Feature importance analysis revealed that curing age and cement content collectively account for 66% of the predictive variance, while water content exhibits the expected negative correlation with strength development. However, uncertainty quantification through Monte Carlo dropout revealed systematic undercalibration, achieving only 38.4% coverage of the nominal 95% prediction interval. These findings provide engineers with quantitative insights for mixture design optimization while emphasizing the importance of uncertainty awareness in computational predictions for structural applications.
Pile foundations are essential to transmit structural loads to the underlying soils and rocks. Piles may functionally or accidently be subjected to lateral soil movements, most often due to the slope's failing nature, deep excavation, soil liquefaction and seismic activities, which significantly affect its stability and safety. This study uses PLAXIS 3D software to predict the response of pile foundations subjected to lateral soil movements. Two case studies were analysed to validate the predictive ability of the software, to test the sensitivity of the input parameters, and to serve as a practical guide for the selection of the parameters in case of lack of availability of complete in-situ information. The behaviour of soil-pile interaction in different types of soils, particularly clay soils, was also considered. The results underline the importance of advanced modelling and accurate parameter selection for the stability and reliability of pile foundations under passive loading and lateral soil movement conditions.