Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks

This paper analyses the accuracy of a selection of expressions currently available to estimate the in-plane shear strength of reinforced masonry (RM) walls, including those presented in some international masonry codes. For this purpose, predictions of such expressions are compared with a set of xperimental results reported in the literature. The experimental database includes specimens built with ceramic bricks and concrete blocks tested in partially and fully grouted conditions, which typically present a shear failure mode. Based on the experimental data collected and using artificial neural networks (ANN), this paper presents alternative expressions to the different existing methods to predict the in-plane shear strength of RM walls. The wall aspect ratio, the axial pre-compression level on the wall, the compressive strength of masonry, as well as the amount and spacing of vertical and horizontal reinforcement throughout the wall are taken into consideration as the input parameters for the proposed expressions. The results obtained show that ANN-based proposals give good predictions and in general fit the experimental results better than other calculation methods.This work was supported by the Fondo Nacional de Ciencia y Tecnologia de Chile, (Fondecyt de Iniciacion) [grant number 11121161].Aguilar, V.; Sandoval, C.; Adam Martínez, JM.; Garzón-Roca, J.; Valdebenito, G. (2016). Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks. Structure and Infrastructure Engineering. 12(12):1661-1674. https://doi.org/10.1080/15732479.2016.1157824S16611674121

Evaluating the Behaviour of Centrally Perforated Unreinforced Masonry Walls: Applications of Numerical Analysis, Machine Learning, and Stochastic Methods

The presence of openings greatly affects the response of unreinforced masonry (URM) walls. This topic greatly attracts the attention of many researchers. Perforated unreinforced masonry (PURM) walls under in-plane loads through the truss discretization method (TDM) along with several machine learning approaches such as Multilayer perceptron (MLP), Group of Method Data Handling (GMDH), and Radial basis function (RBF) are described in this paper. A new method named Multi-pier (MP) that is fast and accurate, is used to determine the behavior of PURM walls. The results of the MP method are expressed as a ratio of lateral load-bearing capacity and initial stiffness of PURM walls to the solid wall in order to generalize the obtained results to other PURM walls. The outcomes of the MP method are employed to predict the behavior of PURM walls using various machine learning approaches. Using the validated network with suitable accuracy, empirical functions and curves are presented in an effort to provide a simplified and practical approach to assess the reduction in the load-bearing capacity and initial stiffness of PURM walls. Results indicate that the adjacent piers of opening have a remarkable impact on the overall response of the PURM wall. Finally, the ability of the MP method to conduct stochastic analysis is evaluated. Moreover, the effect of randomness in the mechanical characteristics and their spatial variation within the PURM wall is presented

A novel cross-validated nondestructive evaluation framework for damage detection using acoustic emission

Developing Structural Health Monitoring (SHM) techniques for monitoring and evaluation in civil, mechanical and aerospace applications has a direct impact on public safety, primarily because it is related to reduced downtime and life extension of critical aging components and structures. Such trends are further fueled by the observed shift in modern inspection from "time-based" to "condition-based" maintenance approaches, which promise targeted evaluations when and exactly where they are needed. In this context, the objective of this dissertation is to develop a novel cross-validated framework of using acoustics-based methods for non-destructive testing & evaluation (NDT&E) with the ultimate goal to improve infrastructure condition assessment related primarily to the aerospace industry. This framework is called cross-validated as the primary NDT method of interest, the Acoustic Emission (AE) method, is used in conjunction with several other NDT methods including Guided Ultrasonic Waves (GUW), Digital Image Correlation (DIC) and Infrared Thermography (IRT). The proposed work is built therefore upon the idea of implementing a multimodal NDE approach including both novel hardware integration and data processing techniques that can mitigate existing challenges in reliably using AE in SHM applications. The advantage of designing reliable damage detectors is realized by integrating acoustic features with heterogeneous features that can provide complementary information on the initiation and development of damage. Several demonstrations in static and dynamic conditions of the proposed framework ranging from fundamental plasticity investigations to applied structural analysis are described to demonstrate the efficacy of the novel approach.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Testing of Materials and Elements in Civil Engineering

This book was proposed and organized as a means to present recent developments in the field of testing of materials and elements in civil engineering. For this reason, the articles highlighted in this editorial relate to different aspects of testing of different materials and elements in civil engineering, from building materials to building structures. The current trend in the development of testing of materials and elements in civil engineering is mainly concerned with the detection of flaws and defects in concrete elements and structures, and acoustic methods predominate in this field. As in medicine, the trend is towards designing test equipment that allows one to obtain a picture of the inside of the tested element and materials. Interesting results with significance for building practices were obtained

Nondestructive Testing (NDT)

The aim of this book is to collect the newest contributions by eminent authors in the field of NDT-SHM, both at the material and structure scale. It therefore provides novel insight at experimental and numerical levels on the application of NDT to a wide variety of materials (concrete, steel, masonry, composites, etc.) in the field of Civil Engineering and Architecture