The hybrid Finite-Discrete Element (FDEM) approach combines aspects of both finite elements and discrete elements with fracture mechanics principles, and therefore it is well suited for realistic simulation of quasi-brittle materials. Notwithstanding, in the literature its application for the analysis of concrete is rather limited. In this paper, the proprietary FDEM code ELFEN is used to model concrete specimens under uniaxial compression and indirect tension (Brazilian tests) of different sizes. The results show that phenomena such as size effect and influence of strain-rate are captured using this modeling technique. In addition, a preliminary model of a slab subjected to dynamic shear punching due to progressive collapse is presented. The resulting fracturing pattern of the impacted slab is similar to observations from actual collapse.

The bond between the concrete and bar is a main factor affecting the performance of the reinforced concrete (RC) members, and since the steel corrosion reduces the bond strength, studying the bond behavior of concrete and GFRP bars is quite necessary. In this research, a database including 112 concrete beam test specimens reinforced with spliced GFRP bars in the splitting failure mode has been collected and used to estimate the concrete-GFRP bar bond strength. This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree. Since the selection of regularization parameters greatly affects the fitting of MARS, Kriging, and M5 models, the regularization parameters have been so optimized as to maximize the training data convergence coefficient. Three hybrid model coupling soft computing methods and genetic algorithm is proposed to automatically perform the trial and error process for finding appropriate modeling regularization parameters. Results have shown that proposed models have significantly increased the prediction accuracy compared to previous models. The proposed MARS, Kriging, and M5 models have improved the convergence coefficient by about 65, 63 and 49%, respectively, compared to the best previous model.

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

In this study, in order to improve the seismic performance of existing reinforced concrete (RC) framed structures, various external attachment of corner steel frame configurations was considered as a user-friendly retrofitting method. The external steel frame is designed to contribute to the lateral stiffness and load carrying capacity of the existing RC structure. A six-story building was taken into account. Four different external corner steel frame configurations were suggested in order to strengthen the building. The 3D models of the building with suggested retrofitting steel frames were developed within ABAQUS environment using solid finite elements and analyzed under horizontal loadings nonlinearly. Horizontal top displacement vs loading curves were obtained to determine the overall performance of the building. Contributions of steel and RC frames to the carried loads were computed individually. Load/capacity ratios for the ground floor columns were presented. In the study, 3D rendered images of the building with the suggested retrofits are created to better visualize the real effect of the retrofit on the final appearance of the façade of the building. The analysis results have shown that the proposed external steel frame retrofit configurations increased the lateral load carrying capacity and lateral stiffness and can be used to improve the seismic performance of RC framed buildings.

This paper investigates the mechanical response of simply supported one-way reinforced concrete slabs under fire through numerical analysis. The numerical model is constructed using the software ABAQUS, and verified by experimental results. Generally, mechanical response of the slab can be divided into four stages, accompanied with drastic stress redistribution. In the first stage, the bottom of the slab is under tension and the top is under compression. In the second stage, stress at bottom of the slab becomes compression due to thermal expansion, with the tension zone at the mid-span section moving up along the thickness of the slab. In the third stage, compression stress at bottom of the slab starts to decrease with the deflection of the slab increasing significantly. In the fourth stage, the bottom of the slab is under tension again, eventually leading to cracking of the slab. Parametric studies were further performed to investigate the effects of load ratio, thickness of protective layer, width-span ratio and slab thickness on the performance of the slab. Results show that increasing the thickness of the slab or reducing the load ratio can significantly postpone the time that deflection of the slab reaches span/20 under fire. It is also worth noting that slabs with the span ratio of 1:1 reached a deflection of span/20 22 min less than those of 1:3. The thickness of protective layer has little effect on performance of the slab until it reaches a deflection of span/20, but its effect becomes obvious in the late stages of fire.

This article deals with the frequency analysis of viscoelastic sandwich disk with graphene nano-platelets (GPLs) reinforced viscoelastic concrete (GPLRVC) face sheets and honeycomb core. The honeycomb core is made of aluminum due to its low weight and high stiffness. The rule of the mixture and modified Halpin-Tsai model are engaged to provide the effective material constant of the concrete. By employing Hamilton's principle, the governing equations of the structure are derived and solved with the aid of the Generalize Differential Quadrature Method (GDQM). In this paper, viscoelastic properties are modeled according to Kelvin-Voigt viscoelasticity. The deflection as the function of time can be solved by the fourth-order Runge-Kutta numerical method. Afterward, a parametric study is carried out to investigate the effects of the outer to inner radius ratio, hexagonal core angle, thickness to length ratio of the concrete, the weight fraction of GPLs into concrete, and the thickness of honeycomb core to inner radius ratio on the frequency of the viscoelastic sandwich disk with honeycomb core and FG-GPLRVC face sheet.

Reinforcement corrosion is the main cause of the durability failure of reinforced concrete (RC) structure. In this paper, a three-dimensional (3D) numerical model of macro-cell corrosion is established to reveal the corrosion mechanisms of steel reinforcement in RC structure. Modified Direct Iteration Method (MDIM) is employed to solve the system of partial differential equations for reinforcement corrosion. Through the sensitivity analysis of electrochemical parameters, it is found that the average corrosion current density is more sensitive to the change of cathodic Tafel slope and anodic equilibrium potential, compared with the other electrochemical parameters. Furthermore, both the anode-to-cathode (A/C) ratio and the anodic length have significant influences on the average corrosion current density, especially when A/C ratio is less than 0.5 and anodic length is less than 35 mm. More importantly, it is demonstrated that the corrosion rate of semi-circumferential corrosion is much larger than that of circumferential corrosion for the same A/C ratio value. The simulation results can give a unique insight into understanding the detailed electrochemical corrosion processes of steel reinforcement in RC structure for application in service life prediction of RC structures in actual civil engineer.