Computer-supported evaluation for seismic performance of existing buildings (original) (raw)
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Uncertainty and Fuzzy Decisions in Earthquake Risk Evaluation of Buildings
Engineering Journal, 2019
The Northern region of Thailand has been considered as one of the seismic risk zones. However, most existing buildings in the area had been designed and constructed based on old building design codes without seismic consideration. Therefore, those buildings are required to upgrade based on earthquake building damage risk evaluation. With resource limitations, it is not feasible to retrofit all buildings in a short period. In addition, the results of the risk evaluation contain uncertain inputs and outputs. The objective of this study is to prioritize building retrofit based on fuzzy earthquake risk assessment. The risk assessment of a building was made considering the risk factors including (1) building vulnerability, (2) seismic intensity and (3) building values. Then, the total risk was calculated by integrating all the risk factors with their uncertainties using a fuzzy rule based model. An example of the retrofit prioritization is shown here considering the three fuzzy factors. The ranking is hospital, temple, school, government building, factory and house, respectively. The result helps decision makers to screen and prioritize the building retrofitting in the seismically prone area.
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.
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.
First-Level Pre-earthquake Assessment of Buildings Using Fuzzy Logic
This paper discusses the performance of a fuzzy logic–based rapid visual screening procedure that results in the categorization of buildings into five different types of possible damage with respect to the potential occurrence of a major seismic event. In order to provide results representing expected damage, adaptive neural networks were used to train the method according to information obtained from the vulnerability of 102 buildings stricken by the Athens earthquake of 1999. The precision of the method was thereby enhanced, implying an improvement in efficiency and presenting remarkable advantages when compared to probabilistic approaches to rapid visual screening. Due to the small size of the database used for the training procedure, however, the prospects of the method remain to be discussed. Nonetheless, by using information from larger databases, the method has the potential for self-improvement, a fact that underlines a good prospect for the formation of reliable and robust pre-earthquake assessment methods.
SEISMIC PERFORMANCE-BASED RELIABILITY OF BUILDING STRUCTURES
SEAOC's Vision 2000 and FEMA 273 defme performance objectives as that of achieving performance levels for given seismic hazard levels. However, due to uncertainties in seismic structural demands and capacities, these objectives are achievable only in a probabilistic sense. Therefore, defmitions of performance objectives must also include associated target reliabilities. Structural and nonstructural performance levels as defined in the Vision 2000 and 1999 Blue Book are somewhat imprecise. Thus, fuzzy sets are used to represent performance levels. A fuzzy relation is constructed between economic loss-to-value ratios and drift ratios. This relation is used to verify performance given the range of expected drift ratios. Limit state equations are proposed to indicate " failure " to achieve the performance level.
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.
Assessment of uncertainties related to seismic hazard using fuzzy analysis
Natural Hazards, 2012
Seismic hazard analysis in the last few decades has become a very important issue. Recently, new technologies and available data have been improved that have helped many scientists to understand where and why earthquakes happen, the physics of earthquakes, etc. Scientists have begun to understand the role of uncertainty in seismic hazard analysis. However, how to handle existing uncertainty is still a significant problem. The same lack of information causes difficulties in quantifying uncertainty accurately. Usually, attenuation curves are obtained in a statistical manner: regression analysis. Statistical and probabilistic analyses show overlapping results for the site coefficients. This overlapping takes place not only at the border between two neighboring classes but also among more than three classes. Although the analysis starts from classifying sites using geological terms, these site coefficients are not classified at all. In the present study, this problem is solved using fuzzy set theory. Using membership functions, the ambiguities at the border between neighboring classes can be avoided. Fuzzy set theory is performed for southern California in the conventional way. In this study, standard deviations that show variations between each site class obtained by fuzzy set theory and the classical manner are compared. Results of this analysis show that when we have insufficient data for hazard assessment, site classification based on fuzzy set theory shows values of standard deviations less than those obtained using the classical way, which is direct proof of less uncertainty.
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...
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.