Liborio Cavaleri

PUMICE CONCRETE FOR STRUCTURAL WALL PANELS

Some properties of lightweight pumice stone concrete (LWPSC) are discussed, on account of a possible structural use of this material. Then the results of an experimental investigation are described, in order to show that pumice can really be considered an alternative to common artificial lightweight aggregates, taking into account the performance pointed out by loading tests carried out on structural systems made of LWPSC. Three different kinds of reinforced wall panels were made using LWPSC, lightweight expanded clay concrete and normal weight concrete; then their structural responses under horizontal cyclic and constant vertical forces were compared, above all with reference to lateral stiffness, cracking pattern, ultimate strength and associated plastic deformations. This comparison shows the effectiveness of pumice as an aggregate in manufacturing concrete, at least for this type of structural element.

A new dynamic identification technique: application to the evaluation of the equivalent strut for infilled frames

Abstract A new time domain identification technique for systems under Gaussian white noise input is presented, requiring for its application the measurement of the system response but no information about input intensity. The technique proposed is based on the statistic moment equations derived by using a special class of mathematical models named “potential models”. These models allow one to determine fundamental properties of the response statistics, making it possible to identify stiffness and dissipation features of a structural system, and also to determine the excitation input. The technique proposed is here applied to the identification of the strut equivalent to the infill of a single story-single bay frame subjected to lateral loads, showing a reduced effort compared to any procedure based on static experimental tests and a higher reliability of the results compared to identification procedures for the strut based only on mathematical assumptions.

Prediction of the additional shear action on frame members due to infills

Infill masonry walls in framed structures make a significant contribution to the response under seismic actions. With special regard to reinforced concrete (RC) structures, it is known that internal forces modifications caused by the frame–infill interaction may be not supported by the surrounding frame because of the additional shear forces arising at the ends of beams and columns. Such additional forces may lead to the activation of brittle collapse mechanisms and hence their prediction is basic in capacity assessment, especially for structures that disregard the details for seismic zones. In this paper a parametric study is carried out addressed to the prediction of the shear forces mentioned before. The results of this study can be used as a support when the simplified model is adopted consisting in the substitution of infill with an equivalent pin jointed concentric strut, because in this case the structural analysis fails in the prediction of the shear forces in question. Through the paper, in which existing RC infilled frames designed only for vertical loads are discussed, analytical laws, depending on the level of the axial force arising in a concentric strut equivalent to infill, are proposed, the above analytical law allowing to correct the local shear forces in the frame critical sections, which are not predictable in the case of substitution of infill with an equivalent concentric strut.

Prediction of the fundamental period of infilled RC frame structures using artificial neural networks

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.

Masonry infills and RC frames interaction: literature overview and state of the art of macromodeling approach

The issue of the influence of masonry infills within RC frames structures has been widely investigated in the last decades by several researchers. The large interest addressed to this topic depends on the actual observation that when in presence of seismic events, the response of framed structures is strongly conditioned by the interaction with the infill walls, which however are considered as non-structural elements and not included in the models. The influence of masonry infills role in structural response is so much relevant to affect not only the overall strength and the stiffness but it may also radically change the possible collapse mechanisms of the overall structural complex under the effect of strong ground motions. Infill panels may have a beneficial effect on the structural response, being able in some cases to supply the lack of resistance of structures to lateral actions, or an adverse contribution inducing unexpected and dangerous non-ductile collapse mechanisms. However, the studies carried out on this topic have demonstrated that, independently from the beneficial or adverse contribution of masonry infills on structural response, their presence cannot be neglected in structural modelling both in design and verification phases. The paper provides a large literature review regarding the modelling techniques developed in the last decades, going from refined nonlinear FE micromodel approaches to simplified equivalent single or multiple strut macromodels including also different technical code statements. The reliability of these approaches is discussed highlighting advantages and weakness points. Macromodelling approach is particularly pointed out since it constitutes the most attractive technique to perform complex nonlinear analyses (static and dynamic). A state of the art of the main issues regarding equivalent strut identification (stiffness, constitutive law and cyclic behaviour) across scientific literature is provided describing in detail noteworthy aspects of some approaches.

Evaluation of infilled frames: an updated in-plane-stiffness macro-model considering the effects of vertical loads

The influence of masonry infills on the in-plane behaviour of RC framed structures is a central topic in the seismic evaluation and retrofitting of existing buildings. Many models in the literature use an equivalent strut member in order to represent the infill but, among the parameters influencing the equivalent strut behaviour, the effect of vertical loads acting on the frames is recognized but not quantified. Nevertheless a vertical load causes a non-negligible variation in the in-plane behaviour of infilled frames by influencing the effective volume of the infill. This results in a change in the stiffness and strength of the system. This paper presents an equivalent diagonal pin-jointed strut model taking into account the stiffening effect of vertical loads on the infill in the initial state. The in-plane stiffness of a range of infilled frames was evaluated using a finite element model of the frame-infill system and the cross-section of the strut equivalent to the infill was obtained for different levels of vertical loading by imposing the equivalence between the frame containing the infill and the frame containing the diagonal strut. In this way a law for identifying the equivalent strut width depending on the geometrical and mechanical characteristics of the infilled frame was generalized to consider the influence of vertical loads for use in the practical applications. The strategy presented, limited to the initial stiffness of infilled frames, is preparatory to the definition of complete non-linear cyclic laws for the equivalent strut.

Macroelement Model for In-Plane and Out-of-Plane Responses of Masonry Infills in Frame Structures

AbstractA new macroelement model is presented in this paper for the simulation of the in-plane (IP) and out-of-plane (OOP) response of infilled frames subjected to seismic actions. The model consis...

Fundamental period of infilled reinforced concrete frame structures

Abstract The fundamental period of vibration appears to be one of the most critical parameters for the seismic design and assessment of structures. In the present paper, the results of a large-scale analytical investigation on the parameters that affect the fundamental period of reinforced concrete structures are presented. The influence of the number of storeys, the number of spans, the span length, the infill wall panel stiffness and the percentage of openings within the infill panel on the fundamental period of infilled RC frames was investigated. Based on these results, a regression analysis is applied in order to propose a new empirical equation for the estimation of the fundamental period. The derived equation is shown to have better predictive power compared with equations available in the literature.

Frictional Effects in Structural Behavior of No-End-Connected Steel-Jacketed RC Columns: Experimental Results and New Approaches to Model Numerical and Analytical Response

AbstractSteel jacketing of reinforced concrete (RC) columns is a common retrofitting technique used to restore bearing and deformation capacity of buildings presenting structural deficiencies. For ...

Krill herd algorithm-based neural network in structural seismic reliability evaluation

ABSTRACT In this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision.