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.
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.
El objetivo de este estudio fue el uso de la toma de decisiones de criterios múltiples para introducir y clasificar los criterios de diseño de recompensa en proyectos de construcción. En esta investigación se clasifican los criterios de diseño de recompensas en proyectos de construcción. En el presente estudio, para identificar y clasificar los criterios de asignación de recompensas a los empleados se utilizaron los métodos de Demetel y la expansión del desempeño de calidad difusa en dos pasos. Los resultados mostraron que la ética profesional es el criterio más importante para la asignación de recompensas a los empleados en los proyectos de construcción. Los resultados muestran que considerar el clima laboral de los proyectos de construcción, mantener la disciplina y tener compromiso organizacional y ayudar a los demás es muy importante. Además, tener el espíritu de trabajo en equipo y cooperación con los demás es muy importante para trabajar en estos entornos. Uno de los puntos destacables en los hallazgos de este estudio es la menor atención prestada por los gerentes de obra al uso de indicadores de medición de cantidad de mano de obra como criterio para la asignación de recompensas y mayor atención a criterios de calidad como la ética profesional, la creatividad, etc., que muestra la diferencia entre la naturaleza del trabajo y el producto final de esta industria con industrias manufactureras como la fabricación de piezas.
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