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For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA) could be used to make rubber-modified recycled aggregate concrete (RRAC). The characteristics (compressive strength and peak strain) of RRAC with various amounts of RA and RPs after heating at various temperatures were studied in this work. The results show that high temperatures significantly decreased the uniaxial compressive strength (UCS), whereas the addition of RA (e.g., 50%) and RPs (e.g., 5%) can mitigate the negative effect caused by high temperatures. The peak strain can also be improved by increasing the replacement ratios of RA and RP. Support vector regression (SVR) models were trained using a total of 120 groups of UCS and peak strain experimental datasets, and an SVR-based multi-objective optimization model was proposed. The excellent correlation coefficients (0.9772 for UCS and 0.9412 for peak strain) found to illustrate the remarkable accuracy of the SVR models. The Pareto fronts of a tri-objective mixture optimization design (UCS, strain, and cost) were successfully generated as the decision reference at varying temperature conditions. A sensitivity analysis was performed to rank the importance of the input variables where temperature was found as the most important one. In addition, the replacement ratio of RA is more important compared with that of the RP for both the UCS and strain datasets. Among the mechanical properties of concrete, compressive strength and peak strain are two key properties. This study provides guidance for the study of RRAC constitutive models under high temperatures.

This work presents the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns and 3 ordinary concrete-filled steel tube columns. Additionally, a systematic study on the influence of various parameters (e.g., slenderness ratio, axial compression ratio, etc.) was conducted on the seismic performance of the specimens. The results show that all the specimens have good hysteresis performance and a similar development trend of skeleton curve. The influence of slenderness ratio on the seismic index of the specimens is more significant than that of the axial compression ratio and the steel pipe wall thickness. Furthermore, artificial intelligence was applied to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, Random Forest with hyperparameters tuned by Firefly Algorithm was chosen. The high correlation coefficients (R) and low root mean square error values from the prediction results showed acceptable accuracy. In addition, sensitivity analysis was applied to rank the influence of the aforementioned input variables on the seismic performance of the specimens. The research results can provide experimental reference for the application of steel tube recycled concrete in earthquake areas.

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Steam curing parameters (temperature rise rate, sustained temperature time, and sustained temperature) have a great influence on compressive properties of 3D printed concrete. • Anisotropy, a crucial characteristic of 3D printed concrete can be significantly influenced by steam curing. • Machine learning technology shows accurate and reliable performance in predicting compressive strength considering anisotropy and steam curing condition. • Beetle antennae search automatically tunes the hyperparameters of machine learning models. The 3D concrete printing (3DCP) technique piques the curiosity of several researchers and enterprises. However, there are few systematic investigations into how curing conditions influence the mechanical performance of 3DCP. This study aims to investigate the effect of various steam curing conditions (temperature rise rate, retention capacity, and sustained temperature) on the performance properties of 3D printing concrete materials at various ages of curing. A thorough test comprises macroscopic and microscopic analysis was conducted. In addition, the best conditions for steam curing are established for compressive characteristics in different directions. Then the anisotropy of mechanical properties of printed materials are studied under various curing settings. This study has contributed to the theoretical research on the influence of steam curing conditions on printed components. In addition, the experimental results were used to create two machine learning (ML) models and the beetle antennae search (BAS) technique was utilised. According to test data, the model is carried out to achieve the mechanical performance prediction of steam curing concrete. To automatically find optimal hyperparameters of ML models, the BAS algorithm was proposed, providing a solid guarantee for the rapid construction of the model. [ABSTRACT FROM AUTHOR]

The performance of ground anchor often employed in retaining the stability of onshore structures depends largely on its interface shear behavior. Interface characterization using pullout tests on element specimens of ground anchor is a versatile approach for determining its interface shear behavior. This research combines laboratory experiments and data-driven modeling to investigate the effect of free/bond lengths as a critical configuration condition of element specimen on the interface characterization results of ground anchors. Machine Learning models such as back propagation neural network (BPNN), random forest (RF), and support vector regression (SVR) were employed. Moreover, the particle swarm optimization (PSO) algorithm was used to improve the process. The results indicate that variations in free/bond lengths have a negligible effect on the trends of interface bond curves when bond lengths reach 90–100 mm. Moreover, when the bond and free lengths are set to 80 mm and 20 mm, respectively, the configuration conditions of the element specimen have a limited effect on the average interface shear strength. The PSO-BPNN model provided the most accurate predictions, which closely followed the experimental results compared to RF and SVR models. Compared to the other models, it reached a greater correlation coefficient (0.9975) and a lower root-mean-square error (14.89 kPa). Moreover, Partial dependence plot (PDP) is introduced to visualize the established machine learning model. • Study evaluates ground anchor shear behavior using lab tests and machine learning, focusing on free/bond length effects. • Free/bond length variations minimally affect ground anchor bond strength at 90–100 mm lengths. • PSO-BPNN model excels in anchorage bond strength prediction, achieving a 0.9975 correlation coefficient. • Ideal anchor test configuration: 80 mm bond and 20 mm free lengths for consistent shear strength. • PDP effectively illustrates key feature impacts on bond force in PSO-BPNN model predictions. [ABSTRACT FROM AUTHOR]

• The rubber-modified recycled aggregate concrete (RRAC) can be fabricated for low-carbon sustainability. The incorporation of RA and RPs can reduce the strength loss when concrete is exposed to high temperature. • Enhanced peak strain can be obtained through increasing the content of RA and RPs. The properties of RRAC are sensitive to the exposed temperature. • The hyperparameters of machine learning (ML) models were successfully tuned by beetle antennae search (BAS) algorithm. The established ML models possessed high accuracy and good generalisation performance. • The BPNN model possessed highest R value and lowest RMSE value compared to RF, LR, and MLR models, thus it had best prediction performance on the database in this study. The recycled aggregate (RA) and waste rubber particles (RPs) can be combined to prepare rubber-modified recycled aggregate concrete (RRAC) effectively contributing to low-carbon sustainability. However, the mechanical characteristics of RRAC must be investigated before the practical application. To this end, this study focused on the uniaxial compressive strength (UCS) and corresponding peak strain of RRAC with versatile design mixtures (i.e. varying contents of RA and RPs) after exposure to different temperatures ranging from 25 °C (room temperature) to 600 °C. The test results exhibited the negative relationship between UCS and RA replacement ratio, RPs content, and temperature. However, RPs positively affected both the loss of UCS and peak strain when RRAC was exposed to high temperatures. Besides, four machine learning (ML) models were developed based on a relatively comprehensive dataset including 120 groups of experimental results. The beetle antennae search (BAS) algorithm was applied to tune the hyperparameter of ML models. The high correlation coefficients (0.9721 for UCS and 0.9441 for peak strain) were determined in modelling using back propagation neural network (BPNN), presenting its accuracy and reliability. Furthermore, BPNN possessed optimal prediction performance since the lower root mean square error (RMSE) and higher correlation coefficient were obtained compared to the other three ML models (random forest, logistic regression, and multiple linear regression). [ABSTRACT FROM AUTHOR]

• The cement content and curing time positively affect the mechanical properties of GRFP tendon reinforced cemented soil comprising unconfined compressive strength (UCS) and ultimate pullout strengths (T p), whereas the water content generally has a negative effect. • The 10-fold cross-validation and beetle antennae search (BAS) effectively tune the hyperparameters of the support vector regression (SVR) models for UCS and T p. • The outstanding performance of the BAS-SVR model is shown in high correlation coefficients (UCS: 0.988, T p : 0.972) and low RMSE values (UCS: 313 kPa, T p : 167 kPa). • The multi-objective optimisation design solusions for UCS-cost and T p -cost are both successfully obtained through generating Pareto fronts based on MOBAS-SVR. • Sensitivity analysis determines the most significant varible among all the input features is cement content. Rebar reinforced cemented soil is employed widely to solve the weak foundation problem led by sludge particularly. Nowadays, the glass fiber-reinforced polymer (GFRP) becomes a new tendon material instead of steel to avoid the performance degradation resulting from steel corrosion. The interface bond strength of GFRP tendon-reinforced cemented soils (GTRCS) displays its excellent mechanical capacity. Nevertheless, its application is obstructed by the deficient studies between the bond strength and influence factors. Therefore, this study investigates the effects of varying water contents (C w : 50%-90%), cement proportions (C c : 6%-30%), and curing periods (T c : 28 days, 90 days) on both pullout strength (T p) and unconfined compression strength (UCS) of GTRCS. The results showed that the pullout strength and compressive strength were positively related to T c and C c and negatively related to C w. Besides, these experimental results were also utilised to develop support vector regression (SVR) models. The beetle antennae search (BAS) algorithm was used to adjust the SVR's hyperparameters. The high correlation coefficients (0.988 for UCS and 0.972 for T p) proved the reliability of the established BAS-SVR models. In addition, the multi-objective beetle antennae search algorithm (MOBAS-SVR) was developed for bi-objective optimisation designs (UCS-cost and T p -cost). Finally, sensitivity analysis was conducted to range the significance of variables for T p and UCS. [ABSTRACT FROM AUTHOR]