Developments in the field of earthquake engineering over the past few decades have contributed to the development of new methods for evaluating the risk levels in buildings. These research methods are rapid visual screening (RVS), seismic risk indexes, and vulnerability assessments, which have been developed to assess the levels of damage in a building or its structural components. RVS methods have been proposed for the rapid pre- and/or post-earthquake screening of existing large building stock in earthquake-prone areas on the basis of sidewalk surveys. The site seismicity, the soil type, the building type, and the corresponding building characteristic features are to be separately examined, and the vulnerability level of each building can be identified by employing the RVS methods. This study describes, evaluates, and compares the findings of previous investigations that utilized conventional RVS methods within a framework. It also suggests the methods to be used for specific goals and proposes prospective enhancement strategies. Furthermore, the article discusses the time-consuming RVS methods (such as FEMA 154, which requires from 15 to 30 min, while NRCC requires one hour), and provides an overview of the application areas of the methods (pre-earthquake: FEMA 154, NRCC, NZEE, etc.; postearthquake: GNDT, EMS, etc.). This review of the traditional RVS methods offers a comprehensive guide and reference for field practitioners (e.g., engineers, architects), and recommends enhancement techniques (e.g., machine learning, fuzzy logic) for researchers to be used in future improvements.
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.
<p><strong>&#160;Abstract</strong></p><p>Since earthquake-induced damages have been escalating in recent years, and some structures built in Europe are designed without considering seismic design standards or with considering moderate design standards, seismic evaluation of existing buildings is required to perform (Aftabur & Shajib, 2012; Valentina et al., 2018). Determining safety level of existing buildings and performing appropriate precautions&#160;is essential not only to prevent possible life-threatening issues but also to minimize economic losses. There are various methods for determining seismic safety of existing structures. Among these methods, detailed seismic assessment methods are challenging to implement; however, Rapid Visual Screening (RVS) methods are capable to applied relatively simply and in a short time to assess large building stocks. Therefore, existing buildings are needed to be inspected by RVS procedures in order to take necessary precautions before an impending earthquake. This study compares performance of 20 Reinforced Concrete (RC) residential&#160;buildings in Gyor, Hungary, by employing FEMA P-154, JBDPA and RISK-UE RVS methods. The assessment results provide a comparison of the employed methods to identify building damage state and provide the insight needed to decide whether additional existing buildings should be examined in order to reduce earthquake-induced damage.</p><p>&#160;</p><table><tbody><tr><td> <p><strong>Keywords:</strong></p> </td> <td> <p>Rapid Visual Screening; Reinforced Concrete Buildings; Seismic Assessment; Gyor</p> </td> </tr></tbody></table><p>&#160;</p><p>&#160;</p><p><strong>References</strong></p><p>Aftabur, R., & Shajib, U. (2012). Seismic Vulnerability Assessment of RC Structures: A Review. <em>International Journal of Science & Emerging Technologies</em>, <em>4</em>(4), 171&#8211;177.</p><p>Valentina, P., Georgios, T., & Luisa, R. D. M. D. N. E. S. D. S. M. M. (2018). <em>Building stock inventory to assess seismic vulnerability across Europe</em> (978-92-79-86707-1; No. JRC112031). Publications Office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC112031</p>
Most of the Unreinforced Masonry (URM) buildings are quite old in Europe based on "Building stock inventory to assess seismic vulnerability across Europe" (Valentina et al., 2018) report. Following the earthquakes (Albania, Italy, etc.) that occurred in Europe, it was revealed that masonry buildings are extremely vulnerable. While probabilistic and deterministic approaches are important for examining a small number of buildings, they do not offer the opportunity to examine a large building stock in a short period of time. Rapid Visual Screening (RVS) methods are used to identify building pre-and post-earthquake vulnerability. Several RVS techniques have been presented in literature over last 30 years. Recent earthquakes have highlighted critical necessity of a rapid vulnerability assessment method for pre-earthquake warning, mitigation, preparedness, and post-earthquake damage state assessment of existing buildings. These findings demonstrate the importance of using an accurate RVS technique to inspect buildings. Because of the screener's subjectivity, these RVS methods contain uncertainty and vagueness. Fuzzy Inference System (FIS) overcomes nonrandom uncertainty and vagueness by considering building characteristics in terms of their degree of truth. This paper introduces a FIS-based RVS case implementation and compares FIS-based RVS to traditional RVS methods for identifying building damage state taking into account rapid visual assessment reports about damage caused by the 2019 Albania earthquake. To determine the damage states of URM buildings, 25 buildings damaged in the 2019 Albania earthquake were analyzed and processed to use in the applied fuzzy logic mathematical model. Initial findings demonstrate that site-specific FIS-based RVS method outperforms conventional RVS methods in terms of damage state prediction accuracy. Even on availability of new data the reliability of results could be further increased considering self-development capability of this method.
<p>One of the critical research areas of engineering science is the assessment of existing buildings' seismic vulnerability. Since the existing building stock is made up of structures that were built before design codes were developed, taking into account low or moderate design codes may make them vulnerable to an upcoming earthquake. To this end, rapid visual screening (RVS) methods can be used to identify building vulnerability before or after an earthquake. A number of RVS methods have been developed; however, it's critical to assess their accuracy in terms of their ability to reliably classify the state of building vulnerability. Therefore, this study offers an application of conventional RVS methods (FEMA P-154, RISK-UE Project, JBDPA), as well as a comparison of the results of the building safety level classification of 20 existing reinforced concrete buildings from Gy&#337;r, Hungary. Pushover analysis was used as a detailed vulnerability assessment technique in addition to the RVS methods to evaluate one more reinforced concrete building. The findings of pushover analysis and RVS methods are contrasted to demonstrate how effectively RVS methods can be utilized to determine building vulnerability. Additionally, the findings of this study can be used to select an RVS method for commencing a pre-earthquake building assessment of the existing reinforced concrete building stock in the investigation area.</p> <p><strong>Keywords:</strong> earthquake; existing buildings; vulnerability assessment; rapid visual screening; machine learning; building damage state</p>
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