Panagiotis G. Asteris

Mathematical Macromodeling of Infilled Frames: State of the Art

The primary objective of this paper is to present a general review of the different macromodels used for the analysis of infilled frames. A number of distinct approaches in the field of analysis of infilled frames since the mid-1950s have yielded several analytical models. These studies stressed that the numerical simulation of infilled frames is difficult and generally unreliable because of the very large number of parameters to be taken into account and the magnitude of the uncertainties associated with most of them. In this paper, the advantages and disadvantages of each macromodel are pointed out, and practical recommendations for the implementation of the different models are indicated.

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.

On the structural analysis and seismic protection of historical masonry structures

In this paper, a general methodology for the seismic protection of historical structures and monuments is pre- sented. The proposed methodology is applied to one historical and monumental structure, in Cyprus, within the frame of the European Project for the Conservation of Historical Mediterranean Sites by Innovative Seismic-Protection Tech- niques. According to the proposed method, the structure under consideration was analysed with and without the imple- mentation of vibration control devices. The entire vulnerability analysis leads to the development of fragility curves, which determine the possibility of a building to be damaged beyond a specified damage level for strong ground motions. These results are quite important during the analysis and redesign procedure for a historical structure since it gives the op- portunity to investigate several different seismic scenarios with different repair/strengthening decisions.

Numerical investigation of the effect of infill walls on the structural response of RC frames

The work presented herein sets out to investigate numerically, by means of nonlinear finite element analysis, the effect of un-reinforced concrete or masonry infill walls on the overall structural response of reinforced concrete frames under static monotonic and seismic loading. For this purpose, a nonlinear finite element package purpose built for the analysis of concrete structures is employed in order to predict the nonlinear behaviour of both the infill walls as well as the surrounding frame. Specifically, the dynamic response of a bare two-storey, one-bay frame, whose behaviour has been experimentally established in the past through shake-table testing, is first investigated via nonlinear finite element analy- sis. Subsequently, concrete and masonry walls are introduced into the selected frame in order to investigate numerically how important aspects of structural response such as stiffness, load-carrying capacity, deformation profile, cracking, duc- tility and mode of failure of the frame are affected.

Natural Pozzolan as a Partial Substitute for Cement in Concrete

In this paper, the use of natural pozzolan as a partial cement substitute in concrete materials is investigated. By means of a test series, four mixes using three types of natural pozzolan, as well as a Class F fly ash, are evaluated. The ef- fectiveness of each pozzolan in controlling alkali-silica reactions has been studied. Correlations have been revealed be- tween the mechanical properties of the proposed mixes and a Portland cement control mix. The results are also compared with industry standards for mortars made with fly ash and silica fume. The papers findings indicate that one type of pozzolan may be used as a substitute for fly ash, but not for silica fume.

Nonlinear Seismic Response Analysis of Realistic Gravity Dam-Reservoir Systems

A methodology for the earthquake response a nalysis of concrete gravity dam-reservoir systems is presented, giving emphasis at the development of an appropriate nonlinear model capable of reproducing the effects on response of all the forms of nonlinearities present in a realistic system. The numerical simulation of the displacement response history of a real-life system to a known seismic excitation has been performed using the finite element method and specially developed interface elements have been employed to model the discontinuities of the structure. The results obtained demonstrate that the earthquake response of the system is significantly affected by the behaviour at the interfaces between contacting materials.

Anisotropic masonry failure criterion using artificial neural networks

In the last decades, a plethora of advanced computational models and techniques have been proposed on the modeling, assessment and design of masonry structures. The successful application of such sophisticated models necessitates the development of reliable analytical models capable of describing the failure of masonry materials. Nevertheless, there is a lack of analytical models due to the anisotropic and brittle nature exhibited by the masonry materials. In the present paper, the use of neural networks (NNs) is proposed to approximate the failure surface of masonry materials in dimensionless form. The comparison of the derived results with experimental findings as well as analytical results demonstrates the promising potential of using NNs for the reliable and robust approximation of the masonry failure surface under biaxial stress.

On the stability of colonnade structural systems under static and dynamic loading conditions

The structural behavior of colonnade structural systems subjected to static and dynamic loading is investigated to identify the main factors affecting the stability and to improve our understanding of their behaviour. In particular, the discrete element method of analysis is utilised to study the static and dynamic behaviour of a typical section of the two storey colonnade of the Forum in Pompeii. Static analysis indicated that the failure of colonnade structures occur at higher friction angles as the weight above the structure decreases and so a sudden collapse can occur when parts of the monument are disassembled. For the dynamic analysis, the mechanical behavior of the colonnade was investigated for both harmonic and real seismic excitations. For excitations with relatively low dominant frequencies, the primary response is rocking; as the excitation frequency increases, the response becomes more complicated demonstrating both sliding and rocking movements. It was also shown that the construction methods used in ancient times, such as multi-block segmented trabeations and solid block beam, have quite significant impact on the mechanical response of the structures under static and dynamic loading.

Prediction of self-compacting concrete strength using artificial neural networks

Despite the widespread use of self-compacting concrete (SCC) in construction in the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict their strength based on the mix components. This is mainly due to the highly non-linear behaviour exhibited by the compressive strength in relation to the components of the concrete mixtures. In the present paper, the application of artificial neural networks (ANNs) to predict the mechanical characteristics of SCC has been investigated. Specifically, ANN models for the prediction of the 28-days compressive strength of admixture-based self-compacting concrete (based on experimental data available in the literature) are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks for the reliable and robust approximation of the compressive strength of self-compacting concrete.

Interpolation Based Numerical Procedure for Solving Two-Point Nonlinear Boundary Value Problems

The present paper offers a simple, efficient and accurate straightforward numerical method for solving two-point nonlinear boundary value problems (NBVPs), which are frequently encountered in a broad discipline of sciences. The proposed technique is based on interpolation procedures and utilizes the capabilities of readily applicable functions, which are embedded in modern commercial mathematical software (Mathematica). The whole approach is computationally effortless, does not contain the drawbacks of other existing methods related to the solution of NBVPs and under the restriction of a sufficient guess of the interpolation domains is capable of tackling a variety of such problems, originated from different scientific areas. Several applications of the method produce results in excellent agreement with existing ones of the relevant literature, the latter being the product of far more sophisticated algorithms.