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Runoff Estimation Based on Stochastic Optimization Method Original Article, C48 Nikbakht Shahbazi A. Journal. Civil Eng. Urban. 3(5):323-330. 2013 ABSTRACT:Despite widespread use of Curve Number (CN) as main parameter in SCS equations for estimating runoff of basins with limited data, its capability in appropriate estimation of runoff has always been controversial. In this study, ability of CN method has been studied in estimating runoff through the application of a stochastic optimization method and within the framework of SCS equations. In stochastic method, sensitivity of statistical distributions, number of statistical samples and range of CN changes on accuracy of calculations have been studied through the application of Monte Carlo Simulation. The used stochastic method is based on the rainfall and runoff information. For this purpose, rainfall and runoff modeling are first selected and probable distribution functions, governing these events, are specified. Then, rainfall and runoff modeling are produced in different sample sizes within random modeling. Produced samples of rainfall and runoff are evaluated based on the specific criteria and specific runoff modeling is selected. The selective runoffs and number of experimental curves are used in SCS equations, rainfall modeling is calculated and their probable functions are determined. Based on Kolmogrov-Smirnov Test, optimal curve number (CNOPT) is set between produced distribution functions and initial probability distribution function governing rainfall events. Also, average and mean curve number (CNAVG,,CNMEG) has been studied. Statistics of basins representing Kasilian and Emameh (Northen Iran) has been used with the aim of studying efficiency of stochastic method. In Kasilian Basin, the runoffs, calculated through the use of CN OPT, enjoyed the minimum error than observational runoffs. This emphasizes desirable capability of CN method in estimating runoff of the basin but stochastic method was unenforceable in Emameh Basin due to the shortage of rainfall and runoff events. Keywords:Curve Number, Monte Carlo Simulation, SCS Method, Statistical Distribution Function, Stochastic Method. منبع: [Hidden Content] دانلود: [Hidden Content].,48-323-330.pdf J. Civil Eng. Urban.,48-323-330.pdf

Analytical Evaluation of Uncertainty Propagation in Seismic Vulnerability Assessment of Tehran Using GIS Original Article, A2 Jahanpeyma MH, Delavar M.R. Journal. Civil Eng. Urban. 1(1): 05-09. 2011. ABSTRACT: One of the properties of geospatial information systems is their use in supporting spatial decision making under uncertainty. It is a complex process which is considered in different situations. The existence of uncertainty in geospatial data and various analyses has the potential to expose users to undesirable consequences in their decision making. Nowadays, natural disasters, particularly earthquake, are among the most important disturbances to sustainable development of countries and governments try to manage them in an optimum manner. They would typically try to decrease the amount of financial damages and loss of lives that would occur because of the events. In this research, geospatial information science/system has been implemented to estimate the seismic vulnerability and its probable damages for a particular scenario in Tehran. We applied fuzzy logic concepts to well-known analytical hierarchical process for the damage assessment. Based on this modified approach we developed a hierarchy of effective factors in earthquake vulnerability due to definition of their priorities against a given earthquake scenario.The effect of uncertainty in geospatial data and analysis functions which are applied in estimating Tehran seismic vulnerability would affect the quality of decision making for estimating the damages. In this paper, we analyzed the implemented geospatial data of population statistics, building information, maps, digital terrain models, and satellite images in the process of studying Tehran’s seismic vulnerability. In this research, we used Monte Carlo simulation approach for uncertainty modeling. We compute the statistical parameters for seismic vulnerability in various iterations of Monte Carlo process. After that we extract the relationship between the number of Monte Carlo process and relative variation of seismic vulnerability’s layer variance. For instance, in order to achieve double precision, the number of iterations must increase four times. Keywords: Uncertainty, GIS, Seismic Vulnerability, Analytic Hierarchy Process, Monte Carlo Simulation منبع: [Hidden Content] دانلود: [Hidden Content],%20A2,final.pdf JCEU, A2,final.pdf

Risk-based management of occupational safety and health in the construction industry – Part 2: Quantitative model Vitor Sousa ⇑, Nuno M. Almeida, Luís A. Dias Department of Civil Engineering, Architecture and GeoResources, Technical University of Lisbon – IST, Av. Rovisco Pais, 1049-001 Lisbon, Portugal a r t i c l e i n f o Article history: Received 6 February 2014 Received in revised form 1 July 2014 Accepted 2 January 2015 Available online 29 January 2015 Keywords: Occupational safety and health Quantitative risk assessment Construction industry Monte Carlo Simulation a b s t r a c t During the last decades, there has been a growing awareness about occupational safety and health risks by the various interested parties in the construction industry. However, despite the substantial improvements achieved, the rate of accidents is still significantly higher than in most of the other industries. Two major reasons have been used to explain this high rate of accidents in the construction industry: (i) the intrinsic riskiness due to the nature of the activities and the particular characteristics of constructions projects and organizations; and (ii) the financial and economic issues regarding the implementation of additional safety measures in a growing competitive market. This companion paper is presented in two parts. The present document refers to Part 2 and makes use of the background knowledge and existing initiatives reviewed in Part 1 to propose and detail the Occupational Safety and Health Potential Risk Model (OSH-PRM). The proposed model was conceived to assist in conducting cost-benefit analysis for occupational safety and health risk management. The OSH-PRM enables an enhanced management of the resources available to improve safety and health conditions in the various activities and for different groups of workers involved in the execution stage of a construction project. 1-s2.0-S0925753515000041-main.pdf