The determination of the fundamental period of vibration of a structure is essential to earthquake design. Current codes provide formulas for the approximate estimation of the fundamental period of earthquake-resistant building systems. These formulas are dependent only on the height of the structure or number of storeys without taking into account the presence of infill walls into the structure, despite the fact that infill walls increase the stiffness and mass of the structure leading to significant changes in the fundamental period. Furthermore, such a formulation is overly conservative and unable to account for structures with geometric irregularities. In this study, which comprises the companion paper of previous published research by the authors, the effect of the vertical geometric irregularities on the fundamental periods of masonry infilled structures has been investigated, through a large set of infilled frame structure cases. Based on these results, an attempt to quantify the reduction of the fundamental period due to the vertical geometric irregularities has been made through a proposal of properly reduction factor.

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

It is well known that masonry infill walls, although non-engineered and considered as non-structural, may provide most of the earthquake resistance and prevent collapse of relatively flexible and weak RC structures.The objective of this study is to investigate the effect of masonry infill walls with openings on the performance of reinforced concrete frames under lateral loads.Specifically, this study is concerned with the evaluation of inelastic parameters of masonry infilled reinforced concrete buildings.Buildings with various heights are selected and designed according to EC8.Inelastic pushover analyses are performed and local and global strength and inelastic deformation characteristics are evaluated.The influence of various parameters, such as the number of storeys, the distribution of infill walls, their strength and their opening percentage on the seismic response of the buildings is thoroughly examined.The results from nonlinear pushover analyses indicate that the overstrength and ductil ity of RC buildings can be higher than the values considered in the design.The presence of infill walls increases considerably the stiffness of the structures and their global resistance to lateral loads.However, the irregular distribution of infills (soft storey -case of pilotis) significantly reduces the capacity of the structure.

Infill-frame interaction constitutes a still, open question both in research and in practicing engineering. Computational models used to predict this interaction are, in most cases, addressing the estimation of the response of the infilled frames when subjected to actions parallel to their plane. However, the observation of the post-earthquake damage has demonstrated that infills, weakened by the in-plane actions, may fail out-of-plane increasing the risks associated to the earthquake scenarios. In spite of this, different studies have shown that infills, if properly designed and supported by the frame, exhibit a significant strength and displacement capacity when called to resist to out-of-plane actions, offering the possibility to develop an arching mechanism in their deformed configuration. The prediction of the combined in plane out-of-plane response prefigures the new goal of the seismic assessment of masonry infilled frames.This paper presents an in-depth literature review of the capacity models developed for the prediction of the out-of-plane response of infilled frames, from the first flexural based computational models to the models implementing the arching action theory in their formulation. A comparison between the results obtainable is provided in order to compare the models reliability against the results of different experimental tests. A final discussion is devoted to the effectiveness the recent integrated in-plane/out-of-plane macromodels used in 3D structural models. A new promising approach, based on the use nonlinear fiber section elements, is also outlined providing a numerical testing of the capacity of such elements to naturally account for the out-of-plane arching mechanism. (C) 2016 Elsevier Ltd. All rights reserved.

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electrodischarge Machined Components.

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

Despite the fact that the fundamental period appears to be one of the most critical parameters for the seismic design of structures according to the modal superposition method, the so far available in the literature proposals for its estimation are often conflicting with each other making their use uncertain. Furthermore, the majority of these proposals do not take into account the presence of infills walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period numerical value. Toward this end, this paper presents a detailed and in-depth analytical investigation on the parameters that affect the fundamental period of reinforce concrete structure. The calculated values of the fundamental period are compared against those obtained from the seismic code and equations proposed by various researchers in the literature. From the analysis of the results it has been found that the number of storeys, the span length, the stiffness of the infill wall panels, the location of the soft storeys and the soil type are crucial parameters that influence the fundamental period of RC buildings.