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Sara Heidarie Golafzani Abolfazl Eslami 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2020,14(3):216-230
ABSTRACT Since piles are one of the major geotechnical foundation systems, estimation of their axial bearing capacity is of great importance. Employing different design methods, resulting in a wide range of bearing capacity estimations, complicates the selection of an appropriate design scheme and confirms the existence of model error along with the inherent soil variability in bearing capacity prediction. This paper tends to evaluate different predictive methods in Reliability-Based Design (RBD) framework. In this regard, different static analyses, SPT and CPT-based methods are considered to evaluate which approaches collectively and which method individually, have more reliable predictions for compiled data bank. In order to assess reliability indices and resistance factors, two approaches have been considered, i.e. First Order Second Moment method (FOSM) and First Order Reliability Method (FORM). To investigate the reliability indices for different methods in both RBD approaches, various safety factors and loading ratios have been considered. Also, the Load and Resistance Factor Design (LRFD) resistance factors are calibrated for different target reliability indices and loading ratios. Results show that CPT-based methods are more reliable among other methods. Furthermore, the estimated efficiency ratio, i.e. the ratio of resistance factor to resistance bias factor, confirms this agreement. 相似文献
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Afzali-Gorouh Zahra Faridhosseini Alireza Bakhtiari Bahram Mosaedi Abolfazl Salehnia Nasrin 《Natural Hazards》2022,114(1):77-99
Natural Hazards - Due to the impacts of climate change on probable maximum precipitation (PMP) and its importance in designing hydraulic structures, PMP estimation is crucial. In this study, the... 相似文献
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Jaafari Abolfazl Rahmati Omid Zenner Eric K. Mafi-Gholami Davood 《Natural Hazards》2022,114(1):457-473
Natural Hazards - The aim of this study was to improve our understanding of factors that affect the spatial distribution of wildfire occurrences at the regional scale. We employed the random... 相似文献
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H. R. Vosoughifar A. Shamsai T. Ebadi 《International Journal of Environmental Science and Technology》2005,1(4):317-323
One of the most important pollutants of groudwaters is nitrate. Different human activities including the application of chemical fertilizers in agriculture, causes the emission of nitrate into groudwaters. In this paper, the dynamic effect of soil moisture on carbon and nitrogen cycles has been analyzed by presenting a connection between soil moisture sample and nonlinear differential equations. At present, wide researches are carried out on modeling soil moisture control in solution flows contain nitrate. In order to do so, separation of energy conservation law equations is carried out by a particular method. The mathematical model governing the nitrate containing current in non-isotropic environment has been presented in the form of combined equations. Equation for distribution in multiple environments and Darcy rule has been considered in this model. Then, using finite volume method, separation of flows contain nitrate in porous media is carried out. The current flux is obtained from central difference approximations or upwind approximation. Mashad plain has been considered for case study at this research. Carrying out calibration operation, the measured results have been contrasted with numerical results of finite volume method. After testing the model, it is possible to foresee the way of nitrate changes in other nodes of calculation network. Using these forecasts, the quality of drinking water for several next years is determined. Carrying out numerical modeling by finite volume method, it is found out that the quality of drinking water of Mashad plain would be suitable for the next ten years. 相似文献
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Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors (LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production (GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve (AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps (LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages. 相似文献
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Binh Thai Pham Abolfazl Jaafari Tran Van Phong Hoang Phan Hai Yen Tran Thi Tuyen Vu Van Luong Huu Duy Nguyen Hiep Van Le Loke Kok Foong 《地学前缘(英文版)》2021,12(3):101105
Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans. 相似文献
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Binh Thai Pham Indra Prakash Abolfazl Jaafari Dieu Tien Bui 《Journal of the Indian Society of Remote Sensing》2018,46(9):1457-1470
In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management. 相似文献
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Abolfazl Jaafari 《Environmental Earth Sciences》2018,77(2):42
The present study investigates a potential application of different resolution topographic data obtained from airborne LiDAR and an integrated ensemble weight-of-evidence and analytic hierarchy process (WoE–AHP) model to spatially predict slope failures. Previously failed slopes of the Pellizzano (Italy) were remotely mapped and divided into two subsets for training and testing purposes. 1, 2, 5, 10, 15, and 20 m topographic data were processed to extract nine terrain attributes identified as conditioning factors for landslides: slope degree, aspect, altitude, plan curvature, profile curvature, stream power index, topographic wetness index, sediment transport index, and topographic roughness index. Landslide (slope failure) susceptibility maps were produced using a single WoE (Model 1), an ensemble WoE–AHP model that used all conditioning factors (Model 2), and an ensemble WoE–AHP model that only used highly nominated conditioning factors (Model 3). The validation results proved the efficiency of high-resolution (≤ 5 m) topographic data and the ensemble model, particularly when all factors were used in the modeling process (Model 2). The average success rates and prediction rates for Model 2 that used ≤ 5 m resolution datasets were 84.26 and 82.78%, respectively. The finding presented in this paper can aid in planning more efficient LiDAR surveys and the handling of large datasets, and in gaining a better understanding of the nature of the predictive models. 相似文献