Effect of loading-induced damage on chloride ingress behavior of recycled aggregate concrete: A comprehensive review

Recycled aggregate concrete (RAC) has received extensive attention as a green building material. However, the service performance and sustainable potential of RAC have not been fully evaluated by traditional models due to the multi-component coupling mechanism. This study proposed a procedure to optimize the mixture of RAC using machine learning (ML) and multi-objective optimization (MOO) model, which enhances the compressive strength and chloride ion resistance of RAC while minimizing the effect on environmental impact (EI) and life cycle costs (LCC). A database containing 807 experimental samples was established to compare the prediction performance of ML models and swarm intelligence (SI) algorithms. WOA-BPNN has the highest prediction accuracy in predicting RAC compressive strength and electric charge passed, with R2 are 0.9904 and 0.9837 on testing set, respectively. The analysis of the feature importance by SHapley Additive explanation (SHAP) and partial dependence plot (PDP) indicates that cement, water and curing age are the key parameters affecting the compressive strength and electric charge passed. The MOO model established by setting constraints and objective functions can effectively evaluate the trade-offs between compressive strength, electric charge passed, EI and LCC, and all pareto solution sets for bi-objective and tetra-objective provide the optimal mixture scheme. Fly ash is the best supplementary cementitious materials (SCMs), as it can enhance the compressive strength and chloride ion resistance of RAC without increasing EI and LCC. The proposed optimization procedure can improve the efficiency of RAC mixture proportion considering mechanical, durability, EI and LCC. 

Multi objective optimization of recycled aggregate concrete based on explainable machine learning

New insights into the mechanical behavior and enhancement mechanism of micro-steel-fiber-reinforced recycled aggregate concrete through in-situ 4D CT analysis

Review on zero waste strategy for urban construction and demolition waste: Full component resource utilization approach for sustainable and low-carbon

Influence of organic corrosion inhibitors on steel corrosion in concrete under the coupled action of freeze–thaw cycles and chloride attack

The role of internally incorporated nano-silica in recycled aggregate concrete: Modification of transport properties

Water and chloride permeability are two important parameters for the evaluation of recycled aggregate concrete (RAC) durability. In service, the tensile loading plays an extremely important role in the generation and propagation of microcracks inside the RAC. Thus, aiming at understanding the mass transport properties of RAC under the coupled action of chloride salt attack and uniaxial sustained tensile loading, an improved experimental set-up was self-designed. And then a series of tests involving the effects of different uniaxial sustained tensile stress levels (10%, 30% and 50% of ultimate tensile stress, namely λ t = 0.1, 0.3 and 0.5) and different replacement ratios of recycled coarse aggregate (RCA) (0, 30%, 50% and 100% mass replacement of normal coarse aggregate, namely R0, R30, R50 and R100) on the transport properties of RAC were conducted, and their coupling effects were further analyzed. Generally, the results indicate that both the sorptivity and chloride diffusion coefficient of RAC increase with the increase of RCA replacement ratio and tensile stress level. The synergistic effect of RCAs and tensile stress significantly accelerates the process of water and chloride invasion into concrete. The initial sorptivity of R100 specimens at λ t = 0.5 was about 4.44 times that of R0 specimens without loading. Moreover, the chloride diffusion coefficient exhibits a linear relationship with the RCA content, while it has a quadratic relationship with the tensile stress level. • A self-designed testing set-up for tensile loading and mass transport was designed. • Coupled effects of RCAs content and tensile stress level were studied. • Cross-sectional and lateral depths of water penetration into RAC were measured. • Transport properties of RAC increase with the increase of RCA content and tensile stress level. • Addition of RCA was more sensitive to chloride transport than the tensile loading. 

Mass transport properties of recycled aggregate concrete under coupling the action of chloride salt attack and uniaxial tensile loading

In thermal error modeling, traditional methods of system identification modeling based on modern control theory are influenced unfavourably by the boundedness of modeling information (data) and unmodeled dynamics, so it is difficult to improve the robustness and accuracy of the model. With the appearance of big data, a fresh angle of view, however, is provided for improving the model accuracy and robustness. Consequently, A data-driven control method is proposed. A model-free adaptive control algorithm is used to establish the thermal error prediction control system and big data collected in various operating condition are used to train the model. The method is verified on a CNC lathe, the experimental result shows that the method can improve the robustness and accuracy of thermal error prediction and has generality.

Thermal error compensation of CNC machine based on data-driven

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Papers

Effect of loading-induced damage on chloride ingress behavior of recycled aggregate concrete: A comprehensive review

The role of internally incorporated nano-silica in recycled aggregate concrete: Modification of transport properties

Mass transport properties of recycled aggregate concrete under coupling the action of chloride salt attack and uniaxial tensile loading

Thermal error compensation of CNC machine based on data-driven