Seismic resiliency of new buildings has improved over the years due to improved seismic codes and design practices. However, vulnerability of seismically deficient older buildings, designed and built on the basis of older codes of practices, poses a significant threat to life safety and survivability of buildings. It is economically not feasible to retrofit the entire inventory of seismically deficient buildings. Therefore, there is need for a comprehensive plan to identify critical buildings and prioritize their retrofit and upgrading requirements. A risk-based evaluation technique is proposed in this paper to quantify seismic assessment and develop a ranking scheme for reinforced concrete buildings. The seismic hazard, building vulnerability and consequence of failure are handled in hierarchical structures. Some of the input risk parameters, expressed as qualitative and quantitative quantifiers, are transformed into commensurable values. A knowledge-based fuzzy rule base modelling is developed and verified through the use of 1994 Northridge Earthquake data on seismic damage of reinforced concrete buildings. DOI: 10.1193/1.2952767

Risk-Based Seismic Evaluation of Reinforced Concrete Buildings

Prediction of an event at a time series is quite important for engineering and economy problems. Time series data mining combines the fields of time series analysis and data mining techniques. This method creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Time series data mining examines the time series in a phase space. In this paper, a prediction algorithm using time series data mining based on fuzzy logic is proposed. Earthquake prediction has been done from a synthetic earthquake time series by using investigating method at first step ago. Time series has been transformed to phase space by using nonlinear time series analysis and then fuzzy logic has been used to prediction optimal values of important parameters characterizing the time series events. Truth of prediction algorithm based fuzzy logic has been proved by application results.

The Prediction Algorithm Based on Fuzzy Logic Using Time Series Data Mining Method

A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain-oriented survey

Lessons learned from performance of reinforced concrete buildings during previous earthquakes and researches over the last three decades have led to the development of improved codes for the seismic design of buildings. However, the existing buildings are vulnerable because they were designed to the older codes and/or posses structural irregularities. Most of these buildings are still occupied which makes evaluation and retrofit necessary in order to minimize the damage induced by earthquakes. Because of large volume of vulnerable buildings, however, consideration of a risk-based seismic assessment and retrofit prioritization is plausible. Seismic (earthquake) risk is the probability (likelihood) that a specified loss will exceed some quantifiable value during a given exposure time. Seismic risk assessment is intricately dependent on site seismic hazard, building vulnerability, and importance/ exposure factor. This intricate process is modelled through a two-tier heuristics-based approach. The two-tier models utilized a hierarchical structure by incorporating wisdom and intuitive knowledge obtained from practitioners and experts. The Tier 1 model considers building performance modifiers (factors) in congruence with the FEMA 154 rapid visual screening manual including: i) building type, ii) vertical irregularity, iii) plan irregularity, iv) year of construction, and v) construction quality. These performance modifiers can readily be obtained from a walk down survey and

/pdf/seismic-risk-assessment-of-reinforced-concrete-buildings-51fvwj2qwt.pdf

Seismic risk assessment of reinforced concrete buildings using fuzzy based techniques

From time immemorial, nature has acted as an excellent friend to man. The relationship between man and nature has continued to be harmonious for a long period of time. However, as years passed by, the selfish nature of man has had an adverse impact towards environmental aspects. Massive destruction of trees and elimination of forest areas have contributed to nature striking back in various ways. One such way of retaliation deals with occurrence of volcanoes and earthquakes. This in turn leads to huge losses in lives and property across the globe. Hence, there is an urgent need in all sectors across the world to take some counteractive measures in controlling the same. A detailed analysis for identifying the possible causes needs to be carried out.

http://ujconline.net/wp-content/uploads/2013/09/UJEAS-14192-Rs.pdf

A detailed study and analysis of earthquake prediction and prevention

Time series analysis is one of important issues in science, engineering, and so on. Up to the present statistical methods [T. Ozaki, et al. (1998)] such as AR model [T. Ozaki, et al. (1998)] and Kalman filter [S. Arimoto, (1985)] have been successfully applied, however, those statistical methods may have problems for solving highly nonlinear problems. An attempt is made to develop practical methods of nonlinear time series by introducing such soft computing techniques [L.A. Zadeh, (1965), (1973), (1993)] as chaos theory [K. Ito, (1993)], neural network [S. Chen, et al. (1989), M. Funabashi, (1992)], GMDH [A.G. Ivakhenemko, (1968), I. Hayashi, (1995)] and fuzzy modelling [(H.Nomura, et al. (1991), Y. Shi, et al. (1996)]. Using the earthquake input record obtained in Hyogo, the applicability and accuracy of the proposed methods are discussed with a comparison of those results

H. Furuta

Papers

Time series prediction of earthquake input by using soft computing