Filtern
Dokumenttyp
Institut
- Institut für Strukturmechanik (ISM) (59)
- Professur Stochastik und Optimierung (42)
- Graduiertenkolleg 1462 (5)
- Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is) (2)
- Professur Angewandte Mathematik (2)
- Professur Informatik im Bauwesen (2)
- Materialforschungs- und -prüfanstalt an der Bauhaus-Universität (1)
- Professur Modellierung und Simulation - Konstruktion (1)
Schlagworte
- Angewandte Mathematik (49)
- Stochastik (41)
- Strukturmechanik (41)
- Angewandte Informatik (9)
- Computerunterstütztes Verfahren (9)
- Building Information Modeling (6)
- Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications (6)
- Maschinelles Lernen (6)
- Erdbeben (5)
- OA-Publikationsfonds2020 (5)
Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
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.
In construction engineering, a schedule’s input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project.
A topology optimization method has been developed for structures subjected to multiple load cases (Example of a bridge pier subjected to wind loads, traffic, superstructure...). We formulate the problem as a multi-criterial optimization problem, where the compliance is computed for each load case. Then, the Epsilon constraint method (method proposed by Chankong and Haimes, 1971) is adapted. The strategy of this method is based on the concept of minimizing the maximum compliance resulting from the critical load case while the other remaining compliances are considered in the constraints. In each iteration, the compliances of all load cases are computed and only the maximum one is minimized. The topology optimization process is switching from one load to another according to the variation of the resulting compliance. In this work we will motivate and explain the proposed methodology and provide some numerical examples.
The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency
In this paper we present an inverse method which is capable of identifying system components in a hydro-mechanically coupled system, i.e. for fluid flow in porous media. As an example we regard water dams that were constructed more than hundred years ago but which are still in use. Over the time ageing processes have changed the condition of these dams. Within the dams fissures might have grown. The proposed method is designed to locate these fissures out of combined mechanical and hydraulic measurements. In a numerical example the fissures or damaged zones are described by a smeared crack model. The task is now to identify simultaneously the spatial distribution of Young’s modulus and the hydraulic permeability due to the fact, that in regions where damages are present, the mechanical stiffness of the system is reduced and the permeability increased. The inversion is shown to be an ill-posed problem. As a consequence regularizing methods have to be applied, where the nonlinear Landweber method (a gradient type method combined with a discrepancy principle) has proven to be an efficient choice.
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.
Many structures in different engineering applications suffer from cracking. In order to make reliable prognosis about the serviceability of those structures it is of utmost importance to identify cracks as precisely as possible by non-destructive testing. A novel approach (XIGA), which combines the Isogeometric Analysis (IGA) and the Extended Finite Element Method (XFEM) is used for the forward problem, namely the analysis of a cracked material, see [1]. Applying the NURBS (Non-Uniform Rational B-Spline) based approach from IGA together with the XFEM allows to describe effectively arbitrarily shaped cracks and avoids the necessity of remeshing during the crack identification problem. We want to exploit these advantages for the inverse problem of detecting existing cracks by non-destructive testing, see e.g. [2]. The quality of the reconstructed cracks however depends on two major issues, namely the quality of the measured data (measurement error) and the discretization of the crack model. The first one will be taken into account by applying regularizing methods with a posteriori stopping criteria. The second one is critical in the sense that too few degrees of freedom, i.e. the number of control points of the NURBS, do not allow for a precise description of the crack. An increased number of control points, however, increases the number of unknowns in the inverse analysis and intensifies the ill-posedness. The trade-off between accuracy and stability is aimed to be found by applying an inverse multilevel algorithm [3, 4] where the identification is started with short knot vectors which successively will be enlarged during the identification process.