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Valiollah Azizifar
Assistant Professor, Dep. of Environmental Science, Islamic Azad University, Qaemshahr Branch, Iran
Civil Engineering, Building, Construction & Architecture

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The Evaluation of Temporary Shelter Areas Locations Using Geographic Information System and Analytic Hierarchy Process Javad Junian; Valiollah Azizifar
Civil Engineering Journal Vol 4, No 7 (2018): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1248.222 KB) | DOI: 10.28991/cej-03091104

Abstract

Earthquakes are notorious as devastating natural disasters that can result in tragic fatalities and economic loss. The building of earthquake evacuation shelters is an effective way to reduce earthquake consequences and protect lives. In present study, analytic hierarchy process (AHP) was applied as a multiple criteria of decision making (MCDM) method to investigate different shelter sites that belong to a disaster-prone area of the north of Iran. The principles of vulnerable areas, access to roads, firefighting centers, populated areas, fault lines, and medical centers were considered to determine optimal temporary shelter areas locations. With the support of a geographic information system (GIS), the method comprised three steps, i.e. selecting candidate shelters, analyzing the spatial coverage of the shelters, and determining the shelter locations. Finally, a case study was used to demonstrate the application of the multi-criteria model and the corresponding solution method and their effectiveness in planning urban earthquake evacuation shelters. It was found that the “distance from fault line” criterion of 0.429 could be the most effective factor along the others.
Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods Valiollah Azizifar; Milad Babajanzadeh
Civil Engineering Journal Vol 4, No 7 (2018): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.652 KB) | DOI: 10.28991/cej-0309193

Abstract

This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programing (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating Silica Fume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasets are compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) and GEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCC incorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture.
Co-Authors Javad Junian Milad Babajanzadeh