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Fatigue is a phenomenon of gradual, permanent internal changes in a material due to repeated or cyclic loading. The fatigue failure of structural elements may decreases the life of infrastructures, therefore the fatigue life of those structures should be considered. Highway and airfield pavements, bridge decks, offshore supporting structure, machinery foundation etc. are subjected to high cycle repeated loading. The randomness in parameters due to the heterogeneous nature of concrete due to fatigue loading leads to complexities in analysing fatigue failure of reinforced concrete. Probabilistic approach is more dependable for the prediction of fatigue life of reinforced concrete than deterministic approach as it can include variations and uncertainties. In recent years, artificial neural network emerged as a new promising computational tool which adopts a probabilistic approach for modelling complex relationships. The purpose of this study is to extract the data from fatigue tests conducted on reinforced concrete beam to create an artificial neural network predictive model. The developed model can able to predict the critical crack length of reinforced concrete members at which failure occurs by considering the fracture mechanics properties and material properties accountable for the softening behaviour of concrete as input. The developed ANN model and analytical model is capable of predicting the fatigue life of reinforced concrete with reasonable accuracy and in a faster approach.

Structural insulated panels (SIPs) made by sandwiching an insulating material from both sides have been used in buildings to enhance thermal resistance without loss in structural integrity. New innovations to improve its compositeness are also being explored. One method is to use shear connector made of high thermal resistant and ductile materials. This connects two outer wythes through insulation layer. The outer material can be of any type of high compressive strength concrete. These are usually reinforced with steel or carbon or glass fiber. The use of light weight and high strength materials helps to reduce the overall thickness of the structure. As the material of shear connector acts as a thermal bridge across the outer wythes, materials with low U value (thermal transmittance) are preferred. In this paper, an attempt has been made to carry out a comparative study on the performance of SIPs with shear connectors manufactured using different materials.

Masonry infill walls are commonly used as external walls and partition walls in RC frame buildings. RC frames in earthquake regions are usually designed with higher ductility and hence undergo large displacements under horizontal loading which lead to a rapid activation of the significantly stiffer infills. This activation generates an undesired interaction between the frame and the infill resulting in the participation of the masonry infills for load transfer. The seismic performance of masonry infill walls under earthquake loading have been studied experimentally and analytically. Damages under in-plane loading are found to escalate the out-of plane response. Over the years the studies have been extended from steel frames to RC frames and the infill materials have been varied from masonry infill to concrete panels. However, the studies are particularly focused on masonry infill units. With the rising concern for environmental pollution control, alternative green materials are also being recommended as infills. This paper presents a review on the techniques that have been developed to improve the seismic performance of infilled frames. It can be achieved by providing a rigid connection between the infill and the frame or by completely isolating the infill walls from the surrounding frame and by using seismic isolation elements.

The fatigue failure of structural elements subjected to repeated cyclic loading may reduce the life of infrastructures. Heterogeneous nature of concrete and random factors in fatigue testing lead to great variability in fatigue life of concrete. As deterministic approach depends on certain parameters and initial conditions, it is not reliable for the prediction of fatigue life of concrete. In this study, a probabilistic approach using artificial neural network is utilised to predict the fatigue life of plain concrete. An artificial neural network predictive model was developed utilising the data from fatigue tests conducted on plain concrete beams of three different sizes mainly small, medium and large. The model is trained using the available experimental data of small and medium specimen and is validated using available experimental data reported on large specimens. The developed model is able to predict the number of cycles of failure of concrete by considering material and fracture mechanics properties responsible for the softening behavior of concrete as input. This approach is advantageous over other methods as it includes the randomness in the fatigue of concrete and will be able to predict the fatigue life of concrete with reasonable accuracy.

A linearized stress intensity factor (SIF) is derived for concrete through a multiscale approach by considering the predominant process zone mechanisms such as aggregate bridging and microcracking. This is achieved by considering a bridging zone and a microcrack at the macrocrack tip. The bridging zone resists the crack growth through aggregate bridging mechanism. The SIF thus derived is further used in developing an analytical model which predicts the entire crack growth curve for plain concrete by making use of the concepts of dimensional analysis and self similarity in conjunction with the human population growth model. This model is validated using experimental data reported on normal strength, high strength and self consolidating concrete. Through sensitivity analyses it is shown that the specimen size plays an important role in the fatigue crack growth process of concrete. (C) 2017 Elsevier Ltd. All rights reserved.

The fracture process of concrete is characterized by various toughening mechanisms that exist at the macro crack tip. In this study, the crack growth resistance due to the bridging of aggregates (defined as bridging stress) is evaluated by relating the crack opening displacements at the macroscopic scale to the mesoscopic one by considering the fracture toughness and the elastic modulus of the interface between the coarse aggregate and the mortar. The influence of specimen size and the stress ratio on the bridging stress is studied. The effect of the bridging stress on the fatigue crack growth rate is predicted and the results are found to agree well with the experiments for normal and micro concrete. The residual strength of a damaged beam is computed in terms of the moment carrying capacity by considering the bridging resistance offered by the coarse aggregates. (C) 2016 Elsevier Ltd. All rights reserved.