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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.

Electrically conductive cementitious composite (ECCC) can be utilized in traffic detection, structural health monitoring (SHM), and pavement deicing. Graphite is a popular conductive filler given its outstanding electrical resistance behavior, but its flat micro‐surface decreases the friction resistance, reducing mechanical strength. However, slag solids can substitute part of the graphite with their considerable conductivity and superior mechanical characteristics. In this research, 27 ECCC composites are developed incorporated by combined graphite powder (GP), blast furnace slag (GGBS), and steel slag (SS). Flexural, unconfined compressive strength (UCS) experiments, and resistance experiments with failure mode evaluations are carried out to explore the influence of proposed conductive fillers with different fractions. Results show the graphite strongly improves conductivity but dramatically reduces mechanical performance. Both steel slag and GGBS reduced the compressive and flexural strength after their replacement ratio exceeded 20%. Additionally, compared with GGBS, SS demonstrated a better influence on mechanical and conductive performance. A 15 wt% ratio of GGBS, 20 wt% of SS, and 4 wt% of GP are explored as the optimum design with 3.4 MPa of flexural strength, 36 MPa of compressive strength, and 7,779 Ω cm of resistance. Lastly, a microstructural investigation is conducted to explore the mechanical and conductive mechanism of ECCC. [ABSTRACT FROM AUTHOR]

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining. [ABSTRACT FROM AUTHOR]

Self‐compacting concrete (SCC) has developed rapidly in modern concrete structures to accommodate the need of high deformability and considerable resistance to segregation. The lightweight aggregate called scoria is also utilized to manufacture lightweight self‐compacting concrete (LWSCC) to improve the strength‐to‐weight ratio with considerable cost beneficial. Meanwhile, the LWSCC employs fibers (polypropylene [PP] and steel) equipping different replacement ratios of traditional aggregates, the SCC will be invented to reveal balanced fresh property, spalling behavior, mechanical performance especially at elevated temperatures. In this study, eight LWSCC mix designs were explored with varying fiber ratios by volume of PP fiber (0.1, 0.15, 0.20, and 0.25%) and steel fiber (0.25, 0.5, 0.75, and 1.0%). The effects of different fiber types and ratios are tested with fresh properties (slump flow and J‐ring), hardened mechanical performance (compressive and tensile strength at 20°C) and high temperature resistance (compressive and tensile strength, mass loss and spalling) after 28‐day curing at 100, 300, 600, and 900°C. The 0.25% optimum fiber ratio for PP fiber mix and 0.75% for steel fiber mix are determined with balanced fresh properties as well as high‐temperature resistance for the hardened properties. [ABSTRACT FROM AUTHOR]

• A novel method was proposed for predicting permeability and unconfined compressive strength of pervious concrete. • 270 samples were prepared for building the dataset. • Permeable and mechanical properties of pervious concrete were elucidated. • Beetle antennae search was firstly used to tune the hyper-parameters of support vector regression. • The support vector regression model tuned by beetle antennae search algorithm has high prediction accuracy. Pervious concrete is a widely used construction material thanks to its good drainage characteristics. Before application, its most important properties, i.e. the permeability coefficient (PC) and 28-day unconfined compressive strength (UCS) are required to be tested. However, conducting PC and UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, an evolved support vector regression (ESVR) tuned by beetle antennae search (BAS) to accurately and effectively predict the PC and UCS of pervious concrete. To prepare the dataset of the ESVR model, 270 specimens in total were prepared and casted in a controlled environment in the laboratory. The water-to-cement (w/c) ratio, aggregate-to-cement (a/c) ratio, and aggregate size were selected as the crucial influencing variables for the inputs, while PC and UCS were the outputs of this model. The results indicate that both the PC and UCS firstly increased and then decreased with increasing w/c ratio. As the a/c ratio increased, PC increased, while UCS decreased. Moreover, BAS is more reliable and efficient than random hyper-parameter selection for hyper-parameter tuning. A low root-mean-square error (RMSE) and high correlation coefficient (R) indicate a relatively high predictive capability of the proposed ESVR model. The sensitivity analysis (SA) suggests the a/c ratio and aggregate size were the most sensitive variables for UCS and PC, respectively. This pioneering work provides a simple and convenient method for evaluating PC and UCS of pervious concrete. [ABSTRACT FROM AUTHOR]

Summary: This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction. [ABSTRACT FROM AUTHOR]

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN's hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented. [ABSTRACT FROM AUTHOR]

• WGP mortar activated by mechanical, chemical, hydrothermal, and combined treatment approaches. • UCS, flexural strength, ASR expansion, and microstructure were investigated. • The mechanical activation significantly enhanced the mechanical performance. • The chemical activation enhanced strength at early ages but negates ASR expansion. • The hydrothermal activation improved strength and is the best method to diminish the ASR expansion. • The combined activation was the optimal method to increase mechanical performance. Land disposal of waste glass results in hazardous environmental contamination. Such waste material should be reclaimed because it is inert and consequently nondegradable. The main component of glass is silica showing pozzolanic properties in cementitious matrices. Thus, the use of waste glass as a supplementary cementitious material in concrete is a sustainable solution to the land disposal of such waste materials. However, concrete incorporating waste glass suffers from deleterious expansion arising from alkali-silica reaction (ASR). This paper shows the effects of different activation approaches, including mechanical, chemical, hydrothermal, and combined activation, used to mitigate ASR. To this aim, activated waste glass powder (WGP) was produced using the aforementioned approaches and used in concrete as sand replacement at percentages of 0%, 10%, 20%, and 30%. The water to cement ratio remained unchanged for all mixes. Results showed that the combined activation was the optimal approach to increase mechanical property. The hydrothermal activation effectively reduced the detrimental ASR expansion, while the chemical treatment induced excessive expansion even for mixtures with a low WGP content of 10%. Also, microstructural analyses showed erosion on the surface of WGP activated by chemical and combined activation methods. Besides, few CH crystals were observed on 75 μm WGP mortar samples, illustrating the excellent pozzolanic activity on finer WGP. [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 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]

Highlights • The compressive strength of lightweight self-compacting concrete was modelled intelligently. • Beetle antennae search algorithm was firstly used to tune hyper-parameters of random forest. • The importance of different input variables was measured. Abstract Self-compacting concrete (SCC) can achieve compaction into every part of the formwork through its own weight without any segregation of the coarse aggregate. Lightweight concrete (LWC) can reduce the dead load of the structure by incorporating the lightweight aggregate (LWA). In recent years, more and more studies have focused on combining the advantages of SCC and LWC to produce lightweight self-compacting concrete (LWSCC). As one of the most important mechanical properties, uniaxial compressive strength (UCS) values need to be tested before field application of this new material. However, conducting UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, a beetle antennae search (BAS) algorithm based random forest (RF) model to accurately and effectively predict the UCS of LWSCC. This model was developed and verified using data from LWSCC laboratory formulation. Results show that BAS was efficient in searching the optimum hyper-parameters of RF. The proposed BAS-RF model achieved high predictive accuracy indicated by a high correlation coefficient (0.97). In addition, by measuring the variable importance, we conclude that temperature was the most sensitive to UCS development, followed by scoria content and water-to-binder (w/b) ratio, while UCS was less sensitive to fiber content. This pioneering work provides a simple and convenient method for evaluating UCS of LWSCC at varying temperatures. [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]

• A multi-output model was firstly applied for predicting pervious concrete properties. • A bio-inspired algorithm was modified to tune hyperparameters of the multi-output model. • A number of pervious concrete samples were prepared in laboratory to test the model. Pervious concrete can purify water, mitigate storm water runoff and reduce the urban heat island effect due to its larger porosity. However, its highly porous inner structure causes a lower compressive strength in comparison with normal concrete. Therefore it is vital to accurately predict the two basic parameters: permeability coefficient (PC) and uniaxial compressive strength (UCS) before field application to reduce time and cost of a construction project. As traditional mathematical models cannot model the highly nonlinear relationships between PC (or UCS) and its constituents, this study addresses this problem by applying a hybrid artificial intelligence model: multi-output least squares support vector regression (MOLSSVR). This model can also improve the prediction accuracy by utilizing the relationship between the two outputs: PC and UCS. In addition, the hyperparameters of MOLSSVR are tuned by a beetle-antennae search (BAS) algorithm which is modified by incorporating self-adaptive inertia weight and Levy flight. To train the proposed model, a large number of pervious concrete samples with different mixture proportions were prepared in laboratory. The results show that the searching efficiency of the modified BAS is significantly higher than that of BAS. The proposed hybrid model achieves better prediction accuracy than other models in the literature. This method can be used to address other multi-output problems in civil engineering. [ABSTRACT FROM AUTHOR]