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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]

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]

[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]

• 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]