Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs

An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression

In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly

Investigating the Use of Learner-Centered Methods in Teaching Literature in Algerian Higher Education: The Case of Djillali Liabes University Sidi Bel Abbes

This paper reports on a research study that aimed to investigate the literature teaching approaches used by English Department faculty at higher education in Djillali Liabes University in Sidi Bel Abbes. More specifically, this study focuses on the use of learner-centered techniques and methods. For this purpose, a combination of both qualitative and quantitative methods was utilized to collect data from the sample population. Results revealed that regardless of the shift in educational methods, teachers are still skeptical to use them in literature classes for they lack confidence about students’ capacities. Moreover, it has been advised that an extensive use of learner-centered activity would encourage students become more autonomous

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

FEM-Based determination of real and complex elastic, dielectric, and piezoelectric moduli in piezoceramic materials

We propose an enhanced iterative scheme for the precise reconstruction of piezoelectric material parameters from electric impedance and mechanical displacement measurements. It is based on finite-element simulations of the full three-dimensional piezoelectric equations, combined with an inexact Newton or nonlinear Landweber iterative inversion scheme. We apply our method to two piezoelectric materials and test its performance. For the first material, the manufacturer provides a full data set; for the second one, no material data set is available. For both cases, our inverse scheme, using electric impedance measurements as input data, performs well

Qualitatively-improved identified parameters of prestressed concrete catenary poles using sensitivity-based Bayesian approach

Prestressed, spun-cast ultrahigh-strength concrete catenary poles have been used widely for electric train systems; for example, thousands of these poles have been installed along high-speed train tracks in Germany. Given the importance of the functionality of train systems, adequate attention has not been paid to catenary poles in research and the literature. Questions regarding the integrity of catenary poles still exist. This study contributes to identify the actual material properties of the poles of interest because the parameter identification is an essential process for any subsequent evaluation of the integrity of catenary poles. Accordingly, a sensitivity-based Bayesian parameter identification approach is developed to estimate the real material properties of the poles using measurements from multiple experiments and numerical models. This approach integrates the sensitivity of time-dependent measurements into the Bayesian inference, which improves the quality of inferred parameters considerably in comparison with classic Bayesian approaches applied in similar case of studies. Furthermore, the proposed approach combines observations of multiple experiments conducted on full-scale poles using a probabilistic uncertainty framework, which provides informative data used in the parameter identification process. Besides, Bayesian inference quantifies the uncertainty of inferred parameters and estimates the hyperparameters, such as the total errors of the observations. The proposed approach utilizes the efficiency of the transitional Markov Chain Monte Carlo algorithm for sampling from the posterior in both levels of Bayesian inference, namely, the unknown parameters and the hyperparameters. The results show the significant influence of the sensitivity concept in improving the quality of the posterior and highlight the importance of identifying the real material properties during the evaluation of the behavior of existing structures, rather than using the characteristic properties from the datasheet. Applying the proposed approach looks very promising when applied to similar applied case studies.publishe

Automatic generation of business process models from user stories

In this paper, we propose an automated approach to extract business process models from requirements, which are presented as user stories. In agile software development, the user story is a simple description of the functionality of the software. It is presented from the user's point of view and is written in natural language. Acceptance criteria are a list of specifications on how a new software feature is expected to operate. Our approach analyzes a set of acceptance criteria accompanying the user story, in order, first, to automatically generate the components of the business model, and then to produce the business model as an activity diagram which is a unified modeling language (UML) behavioral diagram. We start with the use of natural language processing (NLP) techniques to extract the elements necessary to define the rules for retrieving artifacts from the business model. These rules are then developed in Prolog language and imported into Python code. The proposed approach was evaluated on a set of use cases using different performance measures. The results indicate that our method is capable of generating correct and accurate process models

A Review on Key Pre-distribution Schemes based on Combinatorial Designs for Internet of Things Security

This paper is a review of problems and challenges in the Internet of things (IoT) security. The management of key pre-distribution is a cryptographic challenge in every kind of application where security is worried. In recent years there are many papers have proposed different schemes for security. We classify in this paper the existing solutions of key pre-distribution schemes, in order to categorize them, we draw a taxonomy and describe the key pre-distribution coming from combinatorial design and we give examples for that