First-Level Pre-earthquake Assessment of Buildings Using Fuzzy Logic (original) (raw)
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Geosciences, MDPI, 2022
In order to prevent possible loss of life and property, existing building stocks need to be assessed before an impending earthquake. Beyond the examination of large building stocks, rapid evaluation methods are required because the evaluation of even one building utilizing detailed vulnerability assessment methods is computationally expensive. Rapid visual screening (RVS) methods are used to screen and classify existing buildings in large building stocks in earthquake-prone zones prior to or after a catastrophic earthquake. Buildings are assessed using RVS procedures that take into consideration the distinctive features (such as irregularity, construction year, construction quality, and soil type) of each building, which each need to be considered separately. Substantially, studies have been presented to enhance conventional RVS methods in terms of truly identifying building safety levels by using computer algorithms (such as machine learning, fuzzy logic, and neural networks). This study outlines the background research that was conducted in order to establish the parameters for the development of a fuzzy logic-based soft rapid visual screening (S- RVS) method as an alternative to conventional RVS methods. In this investigation, rules, membership functions, transformation values, and defuzzification procedures were established by examining the data of 40 unreinforced masonries (URM) buildings acquired as a consequence of the 2019 Albania earthquake in order to construct a fuzzy logic-based S-RVS method.
Rapid Assessment of Seismic Vulnerability Using Fuzzy Logic
2008
Conventional rapid visual screening procedures are performed to identify buildings susceptible to earthquake damage. Relevant structural characteristic information is collected and used to determine a structural score, which should indicate if a building requires further investigation. Such screening procedures are not so good at identifying buildings at risk and there appears to be little room for improvement. With the object of investigating if results from screening procedures can be improved, this paper presents an alternative method of interpretation based on fuzzy logic. Initially, the basics of fuzzy logic are explained through an example concerning a building’s condition. The principle is then extended to determine a building’s seismic hazard, structural strength and regularity. By combining these four intermediate variables, a final fuzzy logic damage score is obtained. By applying the procedure to a number of buildings damaged in the 1999 Athens earthquake, results from th...
Post-earthquake assessment of buildings damage using fuzzy logic
Engineering Structures
The present paper develops a methodology based on fuzzy logic for post-earthquake assessment of buildings damage. It derives the global building damage level from that reported information by trained technical staff, after in-situ visual inspection of the main parameters, i.e., the "Structural components" and the "Non-structural components". For illustration purposes, thousands of evaluation forms from post-earthquake survey following the 2003 Boumerdes, Algeria, earthquake (Mw = 6.8) have been collected. According to the standard evaluation form, each component's damage is ranked from D 1 (No damage) up to D 5 (Collapse). The aim is then to derive the global damage level of buildings which should also rank from D 1 to D 5. The paper investigates the effect of the number and weights of fuzzy rules to relate each components' damage level to the global damage level using a single-antecedent weighted fuzzy rule. It investigates also the effect of membership functions values so that it is possible to consider one damage level as the most dominant with highest membership value whereas the rest damage levels are still considered although with lower influence. A genetic algorithm is adopted to optimize the rule weights associated to the components' damage levels. The collected database which covers more than 27,000 buildings is used to train and validate the procedure. The theoretical prediction, obtained by automatic processing of the evaluation form for each building, is compared to the global damage (observed damage) identified by inspectors. Results show that the theoretically-based evaluation is in accordance with the observed values for 90% of the investigated buildings.
Neural Network Techniques for Post-Earthquake Assessment of Buildings
2015
After a strong earthquake the damage in the affected area can be so extended that it is not possible to make all building evaluations only by expert engineers. It is common the tendency of non-expert inspectors to aggravate or to underestimate the real level of damage. But, due to the fact that the damage levels are usually linguistic qualifications such as light, minor, moderate, average, severe, etc., an expert system implemented in a computer for post-earthquake evaluation of building damage has been developed using an artificial neural network and fuzzy sets technique. This expert system allows performing the building damage evaluation by non-experts that participate in a massive survey of buildings. The model considers different possible damages in structural and architectural elements and potential site seismic effects in the ground. It takes also into account the pre-existing conditions that can make the building more vulnerable, such as the quality of construction materials,...
Assessment of Seismic Damage on The Exist Buildings Using Fuzzy Logic
IOP Conference Series: Materials Science and Engineering, 2018
Earthquake as a natural disaster could damage the lives of many people and buildings all over the world. These is micvulnerability of the buildings needs to be evaluated. Accurate evaluation of damage sustained by buildings during natural disaster events is critical to determine the buildings safety and their suitability for future occupancy. The earthquake is one of the disasters that structures face the most. There fore, there is a need to evaluate seismic damage and vulnerability of the buildings to protect them. These days fuzzy systems have been widely used in different fields of science because of its simpli city and efficiency. Fuzzy logic provides a suitable framework for reasoning, deduction, and decision making in fuzzy conditions. In this paper, studies on earthquake hazard evaluation of buildings by fuzzy logic modeling concepts in the literature have been investigated and evaluated, as a whole.
Neuro-fuzzy techniques for the classification of earthquake damages in buildings
Measurement, 2010
The identification of damages produced by severe earthquakes on constructions is important for several reasons such as public safety, economical recourses management, infrastructure and urban planning. After the manifestation of an earthquake, engineers have to evaluate the safety of existing structures and decide the actions to be taken. In this study two techniques are proposed for automatic damage classification in buildings. The inherent information contained in accelerograms is described by 20 seismic parameters. Two classification models of earthquake damages based on artificial neural networks and neuro-fuzzy systems were designed. Furthermore, they were tested for their effectiveness to classify structural, architectural, mechanical–electrical-plumbing and contents damages. The proposed systems were trained and tested with three reinforced concrete frame structures. Results show correct classification rates up to 98%. According to these classification rates these techniques are proven a suitable tool for classification of earthquake damages in structures.
Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritize their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. It might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Visual Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a po...
Computational Tool for Post-Earthquake Evaluation of Damage in Buildings
M.EERI A method and a computational tool oriented to assist the damage and safety evaluation of buildings after strong earthquakes is described in this article. The input of the model is the subjective and incomplete information on the building state, obtained by inspectors which are possibly not expert professionals of the field of building safety. The damage levels of the structural components are usually described by linguistic qualifications which can be adequately processed by computational intelligence techniques based on neuro-fuzzy systems what facilitate the complex and urgent tasks of engineering decision-making on the building occupancy after a seismic disaster. The hybrid neuro-fuzzy system used is based on a special three-layer feedforward artificial neural network and fuzzy rule bases and is an effective tool during the emergency response phase providing decisions about safety, habitability, and reparability of the buildings. Examples of application of the computer program are given for two different building classes.
Sustainability, 2022
Many conventional rapid visual screening (RVS) methods for seismic assessment of existing structures have been designed over the past three decades tailored to site-specific building features. The objective of implementing RVS is to identify buildings most likely susceptible to earthquake-induced damage. RVS methods are utilized to classify buildings according to their risk level in order to prioritize the buildings with high seismic risk. The conventional RVS methods are employed to determine the damage after an earthquake or to make a safety assessment in order to predict the damage that may occur in a building before an impending earthquake. Due to the subjectivity of the screener based on a visual examination, previous re-search has shown that these conventional methods can lead to vagueness and uncertainty. Additionally, since RVS methods were found to be conservative and partially accurate and some ex-pert opinion based RVS techniques do not have the capability of further enhancement, it is recommended to develop RVS methods. Therefore, this paper discusses a fuzzy logic based RVS method development to produce an accurate building features responsive examination method for unreinforced masonry (URM) structures, as well as a way to revise existing RVS methods. In this context, RVS parameters are used in a fuzzy inference system hierarchical computational pattern to develop the RVS method. The fuzzy inference system based RVS method was developed taking into consideration post-earthquake building screening data of 40 URM structures located in Albania following the 2019 earthquake as a case study. In addition, FEMA P-154, a conventional RVS method, was employed to screen selected buildings for comparison to the developed RVS method in this study. The findings of the study revealed that the proposed method with an accuracy of 67.5 percent highly outperformed the conventional RVS method by 42.5 percent.
Energies
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.