In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

The current design codes present an equivalent frame method (EFM) for the analysis and design of two-way slab structures. However, since the EFM was developed to be suitable for two-way slab structures subjected to gravity loads only, it brings many problems in its application to the analysis of two-way slabs to which gravity and lateral loads are applied simultaneously. Therefore, authors proposed the unified equivalent frame method (UEFM) that can analyze the structural behavior of flat-plate slab systems subjected to gravity and lateral loads in their previous studies. In this study, the UEFM was modified to be applicable to the two-way slab system with beams. In addition, the accuracy of the proposed UEFM was then examined by comparing it to the lateral behaviors of the two-way slab specimens.

Hollow center cracked disc (HCCD) in Brazilian test was modelled numerically to study the crack propagation in the pre-cracked disc. The pre-existing edge cracks in the disc models were considered to investigate the crack propagation and coalescence paths within the modelled samples. The effect of particle size on the hollow center cracked disc (HCCD) in Brazilian test were considered too. The results shows that Failure pattern is constant by increasing the ball diameter. Tensile cracks are dominant mode of failure. These crack initiates from notch tip, propagate parallel to loading axis and coalescence with upper model boundary. Number of cracks increase by decreasing the ball diameter. Also, tensile fracture toughness was decreased with increasing the particle size. In this research, it is tried to improve the understanding of the crack propagation and crack coalescence phenomena in brittle materials which is of paramount importance in the stability analyses of rock and concrete structures, such as the underground openings, rock slopes and tunnel construction.

Infill wall strengthening method has been widely used for seismic strengthening of deteriorated reinforced concrete (RC) frame structures with non-seismic details. Although such infill wall method can ensure sufficient lateral strengths of RC frame structures deteriorated in seismic performances with a low constructional cost, it generally requires quite cumbersome construction works due to its complex connection details between an infill wall and existing RC frame. In this study, an advanced seismic strengthening method using externally-anchored precast wall panels (EPCW) was developed to overcome the disadvantage inherent in the existing infill wall strengthening method. A total of four RC frame specimens were carefully designed and fabricated. Cyclic loading tests were then conducted to examine seismic performances of RC frame specimens strengthened using the EPCW method. Two specimens were fully strengthened using stocky precast wall panels with different connection details while one specimen was strengthened only in column perimeter with slender precast wall panels. Test results showed that the strength, stiffness, and energy dissipation capacity of RC frame specimens strengthened by EPCWs were improved compared to control frame specimens without strengthening.

In this paper, the performance of post-installed adhesive bonded anchor embedded in concrete is assessed using numerical simulations. This study aims at studying the influence of parameters on the performance of a chemically bonded anchorage system. Non-linear finite element modelling and simulations are carried out by properly using the material properties and phenomenon. Materials parameters such as characteristic length, fracture energy, damage criteria, tension retention and crack width of concrete and interface characteristics are carefully assigned so as to obtain a most realistic behaviour of the chemical anchor system. The peak strength of two different anchor systems obtained from present numerical studies is validated against experimental results. Furthermore, validated numerical models are used to study the load transferring mechanism and damage progression characteristics of various anchors systems where strength of concrete, strength of epoxy, and geometry and disposition of anchors are the parameters. The process of development of strain in concrete adjacent to the anchor and energy dissipated during the course of damage progression are analysed. Results show that the performance of the considered anchorage system is, though a combined effect of material and geometric parameters, but a clear distinction could be made on the parameters to achieve a desired performance based on strength, slip, strain development or dissipated energy. Inspite the increase in anchor capacity with increase in concrete strength, it brings some undesirable performance as well. Furthermore, the pullout capacity of the chemical anchor system increases with a decrease in disparity among the strength of concrete and epoxy.

Over the past three decades, self-compacting concrete (SCC), which is characterized by its superior rheological properties, has been gradually used in construction industry. It is now recognized that the application of SCC using supplementary cementitious materials (SCM) is highly attractive and promising technology reducing the environmental impact of the construction industry and reducing the higher materials costs. This paper presents an experimental study that investigated the mechanical and durability properties of SCCs manufactured with blended binders including fly ash, slag, and micro-silica. A total of 8 batches of SCCs were manufactured. As series of tests were conducted to establish the rheological properties, compressive strength, and durability properties including the water absorption, water permeability, rapid chloride permeability and initial surface absorption of the SCCs. The influences of the SCC strength grade, blended types and content on the properties of the SCCs are investigated. Unified reactive indices are proposed based on the mix proportion and the chemical composition of the corresponding binders are used to assess the compressive strength and strength development of the SCCs. The results also indicate the differences in the underlying mechanisms to drive the durability properties of the SCC at the different strength grades.

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.