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Showing 4 results for Optimization
Uncertainty in Shear Wave Velocity Based on Standard Penetration Test by Using Error Least Square Model
Hossein Mola-Abasi, Farzin Kalantary,
Volume 6, Issue 2 (4-2013)
Abstract
Shear wave velocity (Vs) is a basic engineering soil property implemented in evaluating the soil shear modulus. Due to a few limitations, sometimes it is preferable to determine Vs indirectly by in situ tests, such as standard penetration test (SPT). However, inaccuracies in measurement or estimation of the influencing parameters have always been a major concern, and thus various statistical approaches have been proposed to subdue the effect of such inaccuracies in predictions of future events. In this article, an innovative approach based on robust optimization has been utilized to enumerate the effect of such uncertainties. In order to assess the merits of the proposed approach a database containing 326 data points of case histories from Adapazari, Turkey were gathered from renowned references. The identification technique used in this article is based on the robust counterpart of the least square problem which is a second order cone problem and is efficiently solved by interior point method. A definition of uncertainty based on frobenius norm of the data is introduced and examined against correlation coefficient of various correlation parameters and optimum values are determined. Finally the results of new correlation are compared with those utilizing a commonly used statistical method and the advantages and possibilities of the proposed correlation over the conventional method are highlighted
Forecasting Surface Settlement Caused by Shield Tunneling Using ANN-BBO Model and ANFIS Based on Clustering Methods
Hadi Fattahi, Zohreh Bayatzadehfard,
Volume 12, Issue 5 (12-2018)
Abstract

Maximum surface settlement (MSS) is an important parameter for the design and operation of earth pressure balance (EPB) shields that should determine before operate tunneling. Artificial intelligence (AI) methods are accepted as a technology that offers an alternative way to tackle highly complex problems that can’t be modeled in mathematics. They can learn from examples and they are able to handle incomplete data and noisy. The adaptive network–based fuzzy inference system (ANFIS) and hybrid artificial neural network (ANN) with biogeography-based optimization algorithm (ANN-BBO) are kinds of AI systems that were used in this study to build a prediction model for the MSS caused by EPB shield tunneling. Two ANFIS models were implemented, ANFIS-subtractive clustering method (ANFIS-SCM) and ANFIS-fuzzy c–means clustering method (ANFIS-FCM). The estimation abilities offered using three models were presented by using field data of achieved from Bangkok Subway Project in Thailand. In these models, depth, distance from shaft, ground water level from tunnel invert, average face pressure, average penetrate rate, pitching angle, tail void grouting pressure and percent tail void grout filling were utilized as the input parameters, while the MSS was the output parameter. To compare the performance of models for MSS prediction, the coefficient of correlation (R2) and mean square error (MSE) of the models were calculated, indicating the good performance of the ANFIS-SCM model.


The effect of management strategies of water resources systems on drought indicators in Jarreh Dam
Mahdi Beshavard, Arash Adib, Seyed Mohammad Ashrafi,
Volume 16, Issue 3 (12-2022)
Abstract
Droughts caused by precipitation deficits and increasing water consumption are intensifying worldwide, with negative economic and environmental consequences. The negative impacts can be mitigated by using optimized reservoir operation patterns and implementing rationing rules during droughts. These approaches involve meeting only a portion of total demand, allowing for water storage and accepting a small current deficit to mitigate severe future shortages. This research presents a case study to determine the operational command curves for Jareh Dam and to investigate the impact of reservoir operation under two management policies, Standard Operating Procedure (SOP) and rationing, on downstream drought indices, an aspect not previously studied. To achieve this, an optimization model coupled with a genetic algorithm was linked to a simulation model to determine the optimal values of command curves and rationing coefficients based on historical inflow data to the reservoir. The performance of the model was evaluated in the Allah River water resources system. In addition, the drought severity index (SDI), SOP performance, and rationing model performance during the base period were evaluated by calculating the objective function value or modified shortage index (MSI) and the resilience, vulnerability, and reversibility indices. The results showed that under the rationing model during the study period, the MSI value improved by 41% compared to the SOP method. In addition, the implementation of the rationing policy significantly improved the vulnerability of the system compared to the SOP method, reducing it from 64% to 26%.

Surface water pollution assessment in Shamil-Takht Plain using WRASTIC index and optimization of quality monitoring network
Majid Dashti Barmaki, Zahra Yazdani Barmaki, Massoud Morsali,
Volume 17, Issue 4 (12-2023)
Abstract
In order to design and optimize the quality monitoring network in areas with several sub-basins, it is necessary to know the criteria that affect them, so that in each sub-basin the presence or absence of a monitoring station and the required parameters can be determined. In this respect, the use of the surface water pollution index, namely WRASTIC, can be effective. The WRASTIC model is a practical and advanced method for assessing the risk and potential of pollution in sub-basins. Due to its role in the drinking water supply of the city of Bandar Abbas, monitoring the quality of the Shamil-Takht study area is very beneficial. Therefore, to assess the risk of pollution in this plain, the basin was divided into 16 sub-basins using Global Mapper software. The WRASTIC index was presented as different layers of information, and its value was calculated for each sub-basin by rating by expert judgement method, weighting by hierarchical analysis method, and merging layers using weighted overlap. The results showed that three sub-basins have high risk and three sub-basins have low risk. Then, according to the condition of the streams in each sub-basin, the pollution index and its importance, the number of quality monitoring stations and the necessary parameters in this area were determined. Accordingly, five stations were added to the existing ten hydrometric stations at different locations. In the final 15 stations, the measurement of general parameters and major ions was included in the proposed agenda. The measurement of parameters such as phosphate/phosphorus and nitrate/nitrite was also included in six sub-basins, and heavy metals in three sub-basins.

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