In order to study the triaxial mechanical behavior of steel fiber reinforced high performance concrete (SFRHPC), the standard triaxial compression tests with four different confining pressures are performed on the cylindrical specimens. Three different steel fiber volumes (0, 1% and 2%) are added in the specimens with diameter of 50 mm and height of 100 mm. Test results show that the triaxial compressive strength and peak strain increase with the increasing of fiber content at the same confining pressure. At the same steel fiber content, the triaxial compressive strength and peak strain increases with the confining pressure. The compressive strength growth rate declines as the confining pressure and steel fiber content increases. Longitudinal cracks are dominant in specimens with or without steel fiber under uniaxial compression loading. While with the confining pressure increases, diagonal crack due to shear is obvious. The Mohr-Coulomb criterion is illustrated can be used to describe the failure behavior, and the cohesive force increases as steel fiber content increases. Finally, the numerical model is built by using the PFC3D software. In the numerical model a index is introduced to reflect the effect of steel fiber content on the triaxial compressive behavior. The simulating stress-strain curve and failure mode of SFRHPC are agree well with the experimental results.

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

To circumvent the constraints of time-consuming experimental methods, numerical simulation can be one of the most effective approaches to investigating chloride diffusion behaviors in concrete. However, except for the effect of the external environments, the transport direction of the chloride cannot be neglected when the concrete is exposed to the marine tidal zone, especially in certain areas of concrete members. In this study, based on Fick's second law, considering the effects of timevarying, chloride binding capacity, concrete stress state, ambient temperature, and relative humidity on chloride diffusion coefficient, the modified one-dimensional, two-dimensional, and three-dimensional novel modified chloride diffusion theoretical models were established through defining the current boundary conditions. The simulated results based on the novel modified multi-dimensional model were compared with the experimental results obtained from some previous pieces of literature. The comparing results showed that the modified multi-dimensional model was well-fitted with experimental data, confirming the high accuracy of the novel modified model. The experimental results in literature showed that the chloride diffusion in the corner area of the concrete structure cannot be simulated by a simple one-dimensional diffusion model, where it is necessary to select a suitable multi-dimensional chloride diffusion model for simulation calculation. Therefore, the novel modified multi-dimensional model established in this study has a stronger applicability for practical engineering.

The bending and buckling effect for carbon nanotube-reinforced composite (CNTRC) beams can be evaluated by developing the theory of third shear deformation (TSDT). This study examines beams supported by viscoelastic foundations, where single-walled carbon nanotubes (SWCNTs) are dispersed and oriented within a polymer matrix. Four patterns of reinforcement are used for the CNTRC beams. The rule of mixtures is assessed for the material properties of CNTRC beams. The effective functionally graded materials (FGM) properties are studied by considering three different uneven distribution types of porosity. The damping coefficient is considered to investigate the viscosity effect on the foundation in addition to Winkler's and Pasternak's parameters. The accuracy of the current theory is inspected with multiple comparison works. Moreover, the effects of different beam parameters on the CNTRC beam bending and buckling over a viscoelastic foundation are discussed. The results demonstrated that the O-beam is the weakest type of CNTRC beam to resist buckling and flexure loads, whereas the X-beam is the strongest. Moreover, it is indicated that the presence of porosity in the beams decreases the stiffness and increases deflection. In comparison, the deflection was reduced in the presence of a viscoelastic foundation.

The use of basalt fibers in various types of fiber-reinforced mortars has been increasing. One of the factors that expands the use of basalt fibers is that it is a natural fiber and therefore the production costs are lower than fibers such as PVA fiber. Basalt fibers have some drawbacks such as reducing the workability of mortars in which basalt fibers are added due to their structure, and negatively affecting the mechanical properties when used above a certain proportional amount depending on the type of mixture. For this purpose, in this study, as a different application, the surface of basalt fibers with different lengths (6 and 12 mm) was treated with Triton X-100 surfactant, and these disadvantages were tried to be reduced. In the study, a two-step method was followed. In the first one, the effectiveness of adding untreated and treated basalt fiber at 1, 1.25, 1.5, 1.75 and 2% by weight to the mortar mixtures was determined by conducting flow spread and flow rate as fresh mortar characteristics. In the second one, microstructural characterization and mechanical tests were performed as hardened mortar properties. The results showed that the flow characteristics of basalt fiber reinforced mortars treated with surfactant improved compared to untreated basalt fiber reinforced mortars. In terms of mechanical properties, the addition of 2% treated basalt fiber by weight to the mixtures allowed to obtain %18, %12, and%48 higher values of compressive, flexural, and tensile strength values, respectively, compared to the same amount of untreated basalt fiber mixtures.

The use of waste tires and industrial wastes such as fly ash (FA) and ground granulated blast furnace slag (GGBS) in concrete is an important issue in terms of sustainability. In this study, the effect of parameters affecting the physical, mechanical and microstructural properties of FA/GGBS-based geopolymer concretes with waste rubber fiber was investigated. For this purpose, the effects of rubber fiber percentage (0.6%, 0.9%, 1.2%), binder (75FA25GGBS, 50FA50GGBS, 25FA75GGBS) and curing temperature (75 ℃, 90 ℃ and 105 ℃) were investigated. The Taguchi-Grey Relational Analysis (TGRA) method was used to obtain optimum parameter levels of rubber fiber geopolymer concrete (RFGC). The slump, fresh and hardened density, compressive strength, flexural strength, static and dynamic modulus of elasticity, ultrasonic pulse velocity (UPV) tests and scanning electron microscopy (SEM) analysis were performed on the produced concretes. The analysis of variance (ANOVA) method was used to statistically determine the effects of the parameters on the experimental results. A confirmation test was performed to test the accuracy of the optimum values found by the TGRA method. With the increase of GGBS percentage, the compressive strength of RFGC increased up to 196%. The increase in rubber fiber percentage and curing temperature adversely affected the mechanical properties of RFGC. As a result of TGRA, the optimum value was found to be A1B3C1. ANOVA results showed that the most effective parameter on the experimental results was the binder with 99% contribution percentage. It is understood from the SEM images that the optimum concrete had a denser microstructure and less capillary cracks and voids. For this study, the use of the TGRA method in multiple optimization has proven to provide very useful and reliable results. In cases where many factors are effective on its strength and durability, such as geopolymer concrete, using the TGRA method allows for finding the optimum value of the parameters by saving both time and cost.

A 3D discrete element model integrating the rough surface contact concept with the flat-joint model is suggested to examine the mechanical characteristics of the interfacial transition zone (ITZ) in concrete. The essential components of our DEM procedure include the calculation of the actual contact area in an element contact-pair related to the bonded factor using a Gaussian probability distribution of asperity height, as well as the determination of the contact probability-relative displacement form using the least square method for further computing the force-displacement of ITZs. The present formulations are implemented in MUSEN, an open source development environment for discrete element analysis that is optimized for high performance computation. The model's meso-parameters are calibrated by using uniaxial compression and splitting tensile simulations, as well as laboratory tests of concrete from the literature. The present model's DEM predictions accord well with laboratory experimental tests of pull-out concrete specimens published in the literature.

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.