dolphin echolocation algorithm
در نشریات گروه فنی و مهندسی-
Considering the recent human activities and the resulting climate change in optimizing the operation of the dam reservoir, the effects of climate change should be noticed. In this research, in order to extract command curves by dolphin echolocation and gravitational search algorithms, the monthly inflow of the reservoir, the reservoir storage volume, and the downstream demand of the reservoir in case of climate change were calculated .The optimal output values of the reservoir of Lar Dam (located in Larijan, Amol City) were determined by the approach of minimizing the total square of the monthly relative deficiencies in supply demand and climate change conditions based on the river flow According to the research, by using HADCM3 and scenarios RCP2.6, RCP4.5, and RCP 8.5, climate change has increased the maximum temperature by 5%, 5.2%, and 6.2%, respectively. It has increased the minimum temperature by 3.5%, 5.6%, 5.17%, and increased precipitation by 8.5%, 9.5%, and 13%, respectively. In addition, the runoff from the intermediate scenarios indicates an increase of 3.3% compared to the base period. Moreover, to examine the water allocation policies required downstream, two future and basic conditions are considered. In this study, reservoir efficiency indices in the conditions of (future) climate change and their corresponding values in the base period were compared. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm, as well as, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased. 9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm. Furthermore, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased 9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively.
Keywords: Dolphin echolocation algorithm, climate change, climate scenarios, Gravitational Search Algorithm, Rainfall-Runoff -
بعلت تخمین دقیق زمان حفاری و برآورد هزینه های اجرایی، پیش بینی نرخ نفوذ در حفاری مکانیزه حایز اهمیت است. از طرفی به دلیل قیمت بالای ماشین حفاری تمام مقطع (TBM)، ارزیابی عملکرد در حفاری با استفاده از این ماشین بسیار اهمیت دارد. یکی از شاخص های ارزیابی عملکرد ماشین TBM، پیش بینی نرخ نفوذ این دستگاه می باشد. طی سالیان اخیر توسط محققین روش ها و روابط متنوعی برای پیش بینی نرخ نفوذ پیشنهاد شده که هر کدام ویژگی های خاص خود را داشته و براساس پارامترهای مربوط به توده سنگ و مشخصات ماشین ارایه شده اند. هدف از نگارش این مقاله توسعه مدل های دقیق پیش بینی برای تخمین نرخ نفوذ TBM با استفاده از الگوریتم های فراابتکاری نظیر الگوریتم ژنتیک، الگوریتم سیستم ایمنی مصنوعی، الگوریتم پژواک صدای دلفین و الگوریتم گرگ خاکستری است. برای ساخت مدل های پیش بینی از 153 داده که شامل: مقاومت فشاری تک محوره سنگ بکر (UCS)، تردی سنگ بکر(BI)، زاویه بین صفحات ناپیوستگی و جهت حفاری TBM (α) و فاصله بین صفحات ناپیوستگی (DPW) به عنوان پارامترهای ورودی استفاده شده است. همچنین برای ارزیابی مدل ها از شاخص های آماری نظیر میانگین خطای مربعات (MSE) و ضریب همبستگی مربع (R2) استفاده شده است. نتایج مدلسازی ها نشان می دهد الگوریتم ژنتیک با مقادیر012/0=MSETrain، 02/0=MSETest ، 9319/0=R2Train و 8473/0=R2Test از دقت قابل قبولی در پیش بینی نرخ نفوذ TBM (نسبت به سایر الگوریتم ها) برخوردار است.
کلید واژگان: نرخ نفوذ TBM، الگوریتم ژنتیک، الگوریتم سیستم ایمنی مصنوعی، الگوریتم پژواک صدای دلفین، الگوریتم گرگ خاکستریOne of the indicators for evaluating the performance of a tunnel drilling machine is predicting the penetration rate of this machine. There are various methods and relationships for predicting the penetration rate, each of which has its own characteristics and are presented based on the parameters related to the rock mass and the characteristics of the machine. In this study, genetic, artificial immune system, dolphin echolocation and grey wolf algorithms were used to predict the penetration rate of TBM. In this regard, the database consists of 153 data (122 data for train and 31 data for test) including parameters of intact rock such as strength and brittleness and rock mass characteristics such as distance between planes of weakness and orientation of discontinuities along with TBM machine performance in Queens tunnel has been collected. Mean square error (MSE) and square correlation coefficient (R2) have been used to estimate the error rate between the developed methods. Considering the key parameters of rock mass and intact rock and TBM, relationships to predict the penetration rate are presented and based on statistical analysis, the best relationship is selected. The results are compared with the real data and the results of other models show that the values penetration rate predicted by the genetic algorithm with MSETrain=0.012, MSETest=0.02, R2Train=0.9319 and R2Test=0.8473,has acceptable accuracy compared to other methods.
Keywords: Penetration rate of TBM, Genetic Algorithm, Artificial immune system algorithm, Dolphin echolocation algorithm, Grey Wolf Algorithm -
International Journal of Optimization in Civil Engineering, Volume:10 Issue: 3, Summer 2020, PP 481 -492
During project planning, the prediction of TBM performance is a key factor for selection of tunneling methods and preparation of project schedules. During the construction, TBM performance need to be evaluated based on the encountered rock mass conditions. In this paper, the model based on a relevance vector regression (RVR) optimized by dolphin echolocation algorithm (DEA) for prediction of specific rock mass boreability index (SRMBI) is proposed. The DEA is combined with the RVR for determining the optimal value of its user-defined parameters. The optimized RVR by DEA was employed to available data given in the open source literature. In this model, rock mass uniaxial compressive strength, brittleness index (Bi), volumetric joint account (Jv), and joint orientation (Jo) were used as the input, while the SRMBI was the output parameter. The performances of the suggested predictive model were tested according to two performance indices, i.e., mean square error and determination coefficient. The results show that the RVR- DEA model can be successfully utilized for estimation of the SRMBI in mechanical tunneling.
Keywords: rock mass boreability, relevance vector regression, dolphin echolocation algorithm, TBM performance, mechanical tunneling -
International Journal of Optimization in Civil Engineering, Volume:10 Issue: 2, Spring 2020, PP 261 -275
The evaluation of seismic slope performance during earthquakes is important, because the failure of slope (such as an earth dam, natural slope, or constructed earth embankment) can result in significant financial losses and human. It is important, therefore, to be able to forecast such displacements induced by earthquake. However, the traditional forecasting methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, new intelligence method, namely relevance vector regression (RVR) optimized by dolphin echolocation (DE) and grey wolf optimizer (GWO) algorithms is introduced to forecast the earthquake induced displacements (EID) of slopes. The DE and GWO algorithms is combined with the RVR for determining the optimal value of its user-defined paramee RVR. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R2) and mean square error (MSE). The obtained results of this study indicated that the RVR-GWO model is a reliable method to forecast EID with a higher degree of accuracy (MSE= 0.0160 and R2= 0.9955).
Keywords: Seismic Slope Performance, Relevance Vector Regression, Dolphin Echolocation Algorithm, Grey Wolf Optimizer Algorithm
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