Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 31, No. 7 (July 2018) 1004-1010    Article in Press

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  PREDICTION OF SHEAR WAVE VELOCITY PROFILE USING GMDH TYPE NEURAL NETWORKS AND GENETIC ALGORITHM
 
O. Ataee, N. Hafezi Moghaddas, G. R. Lashkaripour and M. Jabbari Nooghabi
 
( Received: December 31, 2017 – Accepted: March 09, 2018 )
 
 

Abstract    Shear wave velocity (VS) is one of the essential parameters for site characterization studies. This study aims to generate reliable equations for estimating VS of top 30 m of soil profile (VS30) in absence of site-specific measurements and investigates the relation between VS and some of the geotechnical properties (e.g. the number of SPT blow counts (N), depth (Z) and VS in the upper layer of soil (VSu) in Mashhad. Group method of data handling (GMDH) type neural network optimized using the genetic algorithm (GA) was used to model these relationships. A database containing 1657 data points compiled from 206 boreholes was used for training and testing of the models. The performance of the proposed correlations compared with the previously published correlations for VS showed a considerable improvement in the prediction of VS. Sensitivity analysis of the obtained models was performed to study the influence of input parameters on model output. Results show that VSu is the most important parameter in predicting VS and SPT is a more effective parameter than depth in predicting VS in coarse- grained soils.

 

Keywords    Keywords: Shear-Wave Velocity; Standard Penetration Test; Group Method of Data Handling; Sensitivity Analysis; Mashhad city

 

چکیده    سرعت موج برشی(VS) یکی از پارامترهای اساسی برای مطالعات شناسایی ساختگاه می باشد. هدف این مطالعه، ارائه روابط قابل اعتماد برای پیش بینی سرعت موج برشی در سی متر بالایی پروفیل خاک در نبود آزمایشات خاص ساختگاه و مطالعه رابطه بین سرعت موج برشی و تعدادی از خصوصیات ژئوتکنیکی خاک (از جمله عدد نفوذ استاندارد(N)، عمق(Z) و سرعت موج برشی در لای بالایی هر طبقه از خاک(VSu)) در شهر مشهد می باشد. شبکه عصبی از نوع مدیریت داده ها به روش گروهی(GMDH) بهینه شده به وسیله الگریتم ژنتیک برای مدل سازی استفاده شد. یک مجموعه شامل 1657 جفت داده که از 206 گمانه جمع آوری شده برای آموزش و آزمون مدلها استفاده گردید. عملکرد روابط پیشنهاد شده در مقایسه با روابط پیشنهاد شده قبلی برای سرعت موج برشی، بهبود قابل توجهی در پیش بینی سرعت موج نشان می دهد. آنالیز حساسیت مدلهای بدست آمده برای بررسی تاثیر هر یک از پارامترهای ورودی روی خروجی مدل نیز انجام شد. نتایج نشان داد که VSu مهمترین پارامتر در پیش بینی VS است و SPT در خاکهای درشت دانه پارامتر تاثیرگذارتری نسبت به عمق می باشد.

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