Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

The purpose of this paper is to numerically assess the seismic performance of deteriorated reinforced concrete columns using a new plastic-hinge element. Developing a three dimensional (3D) nonlinear model can be difficult and computationally complex, and there can be problems applying it in the field. Thus, to solve these problems, a plastic-hinge element that could considers the shear deformation of deteriorated reinforced concrete columns was proposed. The developed element was based on the Timoshenko beam model and used two nodes with six degrees of freedom and a zero-length element. Moreover, the developed model could consider the combined effects of corrosion, as demonstrated by the reduced reinforcement area and the loss of bond. Consequently, the numerical procedures developed for evaluating the seismic performance of deteriorated columns were validated by comparing the verification results.

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

The vibration analysis of armchair, zigzag and chiral double-walled carbon nanotubes has been developed by inserting the nonlocal theory of elasticity into thin shell theory. First Donnell shell theory is employed while exercising wave propagation approach. Scale effects are realized by using different values of nonlocal parameters under certain boundary conditions. The natural frequencies have been investigated and displayed for various non-local parameters. It is noticed that on increasing nonlocal parameter, the frequency curve tends to decrease. The frequency estimates of clamped-free boundary condition are less than those of clamped-clamped and simply supported computations. The frequency comparisons are presented for armchair, zigzag and chiral nanotubes. The software MATLAB is used to extract the frequencies of double walled carbon nanotubes.

In this study, mechanical, flexural post-cracking, and torsional behaviors of recycled steel fiber-reinforced concrete (RSFRC) incorporating steel fibers obtained from recycling of waste tires were investigated. Initially, three concrete mixes with different fiber contents (0, 40, and 80 kg/m³) were designed and tested in fresh and hardened states. Subsequently, the flexural post-cracking behaviors of RSFRCs were assessed by conducting three-point bending tests on notched beams. It was observed that recycled steel fibers improve the post-cracking flexural behavior in terms of energy absorption, ductility, and residual flexural strength. What's more, torsional behaviors of four RSFRC concrete beams with varying reinforcement configurations were investigated. The results indicated that RSFRCs exhibited an improved post-elastic torsional behaviors, both in terms of the torsional capacity and ductility of the beams. Additionally, numerical analyses were performed to capture the behaviors of RSFRCs in flexure and torsion. At first, inverse analyses were carried out on the results of the three-point bending tests to determine the tensile functions of RSFRC specimens. Additionally, the applicability of the obtained RSFRC tensile functions was verified by comparing the results of the conducted experiments to their numerical counterparts. Finally, it is noteworthy that, despite the scatter (i.e., non-uniqueness) in the aspect ratio of recycled steel fiber (as opposed to industrial steel fiber), their inclusion contributed to the improvement of post-cracking flexural and torsional capacities.

This article aimed to explore the optimization of mineral admixtures and retarding admixture for high-performance concrete. In essence, fresh concrete can be regarded as a mixture in which both coarse and fine aggregates are suspended in a cement-based matrix paste. Based on this view, the test procedure was divided into three progressive stages of binder paste, mortar, and concrete to explore their rheological behavior and mechanical properties respectively. At each stage, there were four experimental control factors, and each factor had three levels. In order to reduce the workload of the experiment, the Taguchi method with an L9(3⁴) orthogonal array and four controllable three-level factors was adopted. The test results show that the use of the Taguchi method effectively optimized the composition of high-performance concrete. The slump of the prepared concrete was above 18 cm, and the slump flow was above 50 cm, indicating that it had good workability. On the other hand, the 28-day compressive strength of the hardened concretes was between 31.3-59.8 MPa. Furthermore, the analysis of variance (ANOVA) results showed that the most significant factor affecting the initial setting time of the fresh concretes was the retarder dosage, and its contribution percentage was 62.66%. On the other hand, the ANOVA results show that the most significant factor affecting the 28-day compressive strength of the hardened concretes was the water to binder ratio, and its contribution percentage was 79.05%.

This paper investigates wave propagation in functionally graded carbon nano-reinforced composite (FG-CNTRC) plates under the influence of temperature based on Reddy' plate model. The material properties of Carbon Nanotubes (CNTs) are size-dependent, and the volume fraction of CNTs varies only along the thickness direction of the plate for different CNTs reinforcement modes. In addition, the material properties of CNTs can vary for different temperature parameters. By solving the eigenvalue problem, analytical dispersion relations can be derived for CNTRC plates. The partial differential equations for the system are derived from Lagrange's principle and higher order shear deformation theory is used to obtain the wave equations for the CNTRC plate. Numerical analyses show that the wave propagation properties in the CNTRC plate are related to the volume fraction parameters of the CNTRC plate and the distribution pattern of the CNTs in the polymer matrix. The effects of different volume fractions of CNTs and the distribution pattern of carbon nanotubes along the cross section (UD-O-X plate) are discussed in detail.

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.