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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.

Since there is a deficit of raw materials available for construction, concrete is essential in designing concrete structures in the modern world. As a result, the construction sector is now familiar with cutting-edge techniques that utilize waste material that is readily available for partial replacement by substituting alternative aggregates for regular aggregates. In this study, pumice stone located in the lowest section of the ocean or the abyss of red clay is utilized in place of concrete, with a replacement in a portion made of pumice mixed with cement. Concrete’s mechanical and physical durability was examined by measuring its Split and compressive strengths of ordinary concrete and substituting it with varying quantities of pumice (10% to 30%). M sand is entirely replaced in fine aggregate. From the previous studies, it shows the 50% of Coarse aggregate replacement and here we investigate how well partial pumice substitutions for coarse aggregate and M sand substitutions of fine aggregate can gain sufficient strength. Based on the experimental results, the current thesis compares the properties of conventional and replaced concrete for the various percentages of pumice stone replacement to coarse aggregate. It concludes that a 25% partial replacement by pumice yields the maximum compressive strength. We also studied the durability parameters in the present paper.

Currently, fire in building is one of the most serious disasters. With the increase of basic construction items in western China, ordinary medium sand resource no longer met with the need of engineering. Compressive strength experiments of concrete produced with desert sand after elevated temperature were carried out in this paper. The effects of desert sand replacement rate (DSRR), temperature and cooling regime on the mechanical performances of concrete produced with desert sand were analyzed. XRD and SEM experiments were also conducted to study the microstructure of concrete produced with desert sand after elevated temperature. Experimental results showed that the cubic compressive strength of concrete produced with desert sand increased firstly, and then declined with temperature. Whereas, the prismatic compressive strength and elasticity modulus of concrete produced with desert sand under static compression declined with temperature. With the enhancement of DSRR, the elasticity modulus under static compression, cubic compressive strength and prismatic compressive strength of concrete produced with desert sand after elevated temperature increased firstly, and then declined, the maximum value of which was reached when DSRR amounted to 40%. Regression models were established to predict the mechanical performances of concrete produced with desert sand after elevated temperature, which were in good agreement with experimental results.

A review of the tensile strength of rock was conducted to determine the relationship between direct tensile strength (DTS) and Brazilian tensile strength (BTS) and to examine the validity of estimating tensile strength from other measured properties, such as the crack initiation (CI) threshold. A data set was gathered from the existing literature where tensile values could be reliably correlated with unconfined compressive strength or CI values. It was determined that the BTS obtained in standard testing is generally greater than the equivalent DTS and that this relationship is rock type dependent. CI yields a reasonable estimate of tensile strength and this correlation is improved when the BTS values are reduced to DTS values by rock type specific correlations. The factor f , in DTS =  f BTS, can be considered to be approximately 0.9 for metamorphic, 0.8 for igneous and 0.7 for sedimentary rocks. The relationships presented demonstrate that there is wide scatter in the available data for estimating tensile strength likely due to both specimen variability and testing configuration, including platen geometry and relative stiffness. Estimates of tensile strength should only be used for preliminary design purposes and measurements should be made to confirm preliminary assumptions for each design.

In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress–strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of basalt fiber from 0.6 to 1.8 are mixed with High-Performance Concrete with high-volume contact of cement, fine and coarse aggregate. Compressive strengths and compressive stress–strain curves are applied after 7-, 14-, and 28-day curing periods. To find the compressive strength and predict the compressive stress–strain curve, the Logistic Map algorithm was prepared through classical programming. The results of this study prove that the logistic map is able to predict the compressive strength and compressive stress–strain of BFHPC with high accuracy. In addition, various types of methods, such as Coefficient of Determination (R2), are applied to ensure the accuracy of the algorithm. For this purpose, the value of R2 was equal to 0.96, which showed that the algorithm is reliable for predicting compressive strength. Finally, it was concluded that The Logistic Map algorithm developed through classical programming could be used as an easy and reliable method to predict the compressive strength and compressive stress–strain of BFHPC.

Endovascular thrombectomy procedures are significantly influenced by the mechanical response of thrombi to the multi-axial loading imposed during retrieval. Compression tests are commonly used to determine compressive ex vivo thrombus and clot analogue stiffness. However, there is a shortage of data in tension. This study compares the tensile and compressive response of clot analogues made from the blood of healthy human donors in a range of compositions. Citrated whole blood was collected from six healthy human donors. Contracted and non-contracted fibrin clots, whole blood clots and clots reconstructed with a range of red blood cell (RBC) volumetric concentrations (5–80%) were prepared under static conditions. Both uniaxial tension and unconfined compression tests were performed using custom-built setups. Approximately linear nominal stress–strain profiles were found under tension, while strong strain-stiffening profiles were observed under compression. Low- and high-strain stiffness values were acquired by applying a linear fit to the initial and final 10% of the nominal stress–strain curves. Tensile stiffness values were approximately 15 times higher than low-strain compressive stiffness and 40 times lower than high-strain compressive stiffness values. Tensile stiffness decreased with an increasing RBC volume in the blood mixture. In contrast, high-strain compressive stiffness values increased from 0 to 10%, followed by a decrease from 20 to 80% RBC volumes. Furthermore, inter-donor differences were observed with up to 50% variation in the stiffness of whole blood clot analogues prepared in the same manner between healthy human donors.

Using two different test standards (ASTM and BS), the influence of five different sizes of sand on the ultimate stress (MPa) of hand-mixed cement-grouted sands modified with polymer is discussed in this study. The characteristics of cement-grouted sands modified with polymer up to 0.16% (percent weight of dry cement) were evaluated and measured in fresh and hardened conditions. Adding polymer decreased the water/cement ratio ( w / c ) from 0.6 to 0.5, and it kept the flow time of the cement-based grout in the range of 18 to 23 s recommended by ASTM standard. Using mix proportion and curing time, adding polymer significantly increased the prismatic and cylindrical compressive strength (MPa) by 113 to 577% and 53 to 459%. Several mathematical approaches such as linear regression (LR), Nonlinear regression (NLR), multilinear regression (MLR), Artificial neural network (ANN), and M5P-tree were used to predict the compression strength of cement-grouted sand with a different size of sand, w/c, polymer content, and curing age. Based on the scatter index (SI), objective function (OBJ) assessments, and training and testing datasets, the compressive strength of the cement-grouted sands can be predicted well using NLR and ANN models. The compression strength tested using the BS standard was 71% higher than the compression strength of the same mix tested using the ASTM standard.