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An experimental program was conducted to determine the effects of geosynthetic reinforcement on mitigating reflection cracking in asphalt overlays. The objectives of this study were to asses the effects of geosynthetics inclusion and its placement location on the accumulation of permanent deformation. To simulate an asphalt pavement overlaid on top of a crack in a concrete or asphalt pavement, an asphalt mixture specimen was placed on top of two discontinuous concrete or asphalt concrete blocks with 100 mm height. Four types of specimens were prepared with respect to the location of geogrid: (I) Unreinforced samples, which served as control specimen, (II) Samples with geogrid embedded on the concrete or asphalt concrete block, (III) Samples with geogrid embeded one-thired depth of asphalt concrete from bottom, (IV) Samples with geogrid embedded in the middle of the asphalt beam. Each specimen was then placed on the rubber foundation in order to be tested. Simulated- repeated loading was applied to the asphalt mixture specimens using a hydraulic dynamic loading frame. Each experiment was recorded in its entirety by a video camera to allow the physical observation of reflection crack formation and propagation. This study revealed that geosynthetic reinforced specimens exhibited resistance to reflection cracking. Placing the geogrid at the one- third depth of overlay thickness had the maximum predicted service life. Results indicate a significant reduction in the rate of crack propagation and rutting in reinforced samples compared to unreinforced samples.

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Runoff simulation is a vital issue in water resource planning and management. Various models with different levels of accuracy

and precision are developed for this purpose considering various prediction time scales. In this paper, two models of IHACRES

(Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) and ANN (Artificial

Neural Network) models are developed and compared for long term runoff simulation in the south eastern part of Iran. These

models have been utilized to simulate5-month runoff in the wet period of December-April. In IHACRES application, first the

rainfall is predicted using climatic signals and then transformed to runoff. For this purpose, the daily precipitation is downscaled

by two models of SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station-Weather Generator). The

best results of these models are selected as IHACRES model input for simulating of runoff. In application of the ANN model,

effective large scale signals of SLP(Sea Level Pressure), SST(Sea Surface Temperature), DSLP and runoff are considered as model

inputs for the study region. The performances of the considered models in real time planning of water resources is evaluated by

comparing simulated runoff with observed data and through SWSI(Surface Water Scarcity Index) drought index calculation.

According to the results, the IHACRES model outperformed ANN in simulating runoff in the study area, and its results are more

likely to be comparable with the observed values and therefore, could be employed with more certainty.

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The seismic behavior of frame bridges is generally evaluated using nonlinear static analysis with different plasticity models hence this paper tends to focus on the effectiveness of the two most common nonlinear modeling approaches comprising of concentrated and distributed plasticity models. A three-span prestressed concrete frame bridge in Tehran, Iran, including a pair of independent parallel bridge structures was selected as the model of the study. The parallel bridges were composed of identical decks with the total length of 215 meters supported on different regular and irregular substructures with non-prismatic piers. To calibrate the analytical modeling, a large-scale experimental and analytical seismic study on a two-span reinforced concrete bridge system carried out at the University of Nevada Reno was used. The comparison of the results shows the accuracy of analytical studies. In addition, close correlation between results obtained from two nonlinear modeling methods depicts that the lumped plasticity approach can be decisively considered as the useful tool for the nonlinear modeling of non-prismatic bridge piers with hollow sections due to its simple modeling assumption and less computational time.

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Critical non-structural equipments, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., are vulnerable to strong earthquakes, and the failure of these equipments may result in a heavy economic loss. To guarantee function of vulnerable equipment during earthquake peak acceleration and peak base displacement response of system should be limited to allowable levels. Traditional and passive control strategies cannot afford these contradictory targets in same time for broad range of ground motions. In recent years, semi-active control systems have been introduced as an adaptable and reliable alternative to control response under both limitations with low power supply. In this paper, efficacy of smart semi-active controlled floor isolation system which consists of a rolling pendulum system and a semi-active controlled magnetorheological (MR)-damper to control seismic response of equipment has been investigated by using clipped-H_2/LQG and clipped-H_∞ algorithms. The effectiveness of these algorithms was examined for equipment stand on raised floor due to floor motions in seven stories building. The results demonstrate semi-active control effectively decrease response acceleration and velocity of equipment in compare to passive strategy and hold its relative displacement to floor in least value. Furthermore it was shown semi-active control strategy with clipped-H_∞ algorithm in controlling seismic response of equipment compare to clipped-H_2/LQG algorithm and passive strategy (isolation system) have better performance in protecting equipment.