نشریه علوم زمین خوارزمی

نشریه علوم زمین خوارزمی

ارائه روابطی جهت تخمین خصوصیات دینامیکی سنگ آهک با رویکرد تجربی

نویسندگان
امیر آزادمهر
سید محمود کاظمی
محسن صفاریان
دانشگاه بیرجند
10.22034/KJES.2023.9.1.105711
چکیده
خصوصیات دینامیکی و استاتیکی سنگ‌ها برای طراحی سازه‌های ژئوتکنیکی و مدل‌سازی پی‌های سنگی اهمیت زیادی دارد. هدف اصلی این مقاله ارائه روابط منطقه‌ای و جهانی بین مدول الاستیسیته استاتیک و دینامیک با رویکرد تجربی و تخمین سرعت موج برشی سنگ آهک به روش‌های آماری و شبکه عصبی مصنوعی می‌باشد. بدین منظور ابتدا آزمایش‌های پتروگرافی و فیزیکی و مکانیکی بر روی 70 مغزه از سنگ آهک ساختگاه سد کارون 4 انجام شد. سپس بانک داده‌ای از روابط ارائه شده در پژوهش‌های پیشین از نقاط مختلف جهان تهیه شد و روابط جهانی و منطقه‌ای برای سنگ آهک‌های ایران ارائه شد. نتایج آنالیز آماری نشان داد که نسبت مدول الاستیسیته دینامیک به استاتیک برای سنگ آهک‌های مورد مطالعه 5/2 می‌باشد. همچنین نسبت پواسون دینامیک به استاتیک برای این سنگ‌ها 41/1 می‌باشد. مقدار متوسط مدول دینامیک بدست آمده از روابط پژوهشگران مختلف برابر با90/19 گیگاپاسکال است که از مقدار متوسط مدول دینامیک پژوهش حاضر (20/31 گیگاپاسکال) کمتر است. با توجه به دقیق‌ترین برازش رابطه جهانی (R2=0.98, RMSE=7.9, and MAPE=1.67) و منطقه‌ای (R2=0.96, RMSE=5.24, and MAPE=0.91) با دقت خیلی بالا بین مدول الاستیسیته دینامیک و استاتیک ارائه شد. نتایج شبکه عصبی مصنوعی و رگرسیون چند متغیره نشان داد که تخمین سرعت موج برشی بر اساس سرعت موجP ، جذب آب و چگالی با دقت بالایی امکان‌پذیر می‌باشد. نتایج نشان داد که دقت شبکه عصبی (R2=0.98 , RMSE=0.27) بیشتر از روش رگرسیون چند متغیره خطی (R2=0.86 , RMSE=0.39) می‌باشد. همچنین شبکه عصبی در پیش‌بینی این متغیر محافظه‌کارانه عمل می‌کند.
کلیدواژه‌ها
مدول الاستیسیته دینامیک و استاتیک
سرعت موج برشی
سنگ آهک
شبکه عصبی مصنوعی
رگرسیون چند متغیره

عنوان مقاله English

Presenting relationships for estimating dynamic properties of limestone using an experimental approach

نویسندگان English

Amir Azadmehr
Sayed mahhmood Kazemi
Mohsen Saffarian
Birjand
چکیده English

Dynamic and static properties of the rocks are very important for designing geotechnical structures and modeling rock foundations. The main purpose of this paper is to present the regional and global relationships between the static and dynamic elasticity modulus with an experimental approach and to estimate the shear wave velocity of limestone by statistical methods and artificial neural network (ANN). For this purpose, petrographic, physical and mechanical experiments were first conducted on 80 limestone cores from the Karun 4 dam site. A database was then created using the literature data and compared with the results of this study. The results of statistical analysis show that the ratio of dynamic to static modulus of elasticity for the studied samples is 2.5. Also, the ratio of dynamic to static Poisson for these rocks was 1.41. The average value of the dynamic modulus obtained from the literature was equal to 19.90 GPa, which is less than the average value of the dynamic modulus of the present study (31.20 GPa). Due to the most accurate fit, the global relationship (R2 = 0.98, RMSE = 7.9, MAPE = 1.67) and the regional relationship (R2 = 0.96, RMSE = 5.24 MAPE = 0.91) were presented with very high accuracy between the dynamic and static modulus of elasticity. The results of artificial neural network and multivariate regression showed that estimation of shear wave velocity (Vs) based on P-wave velocity, water absorption and density is possible with high accuracy. The results showed that the ANN accuracy (R2 = 0.98, RMSE = 0.27) was higher than the multivariate linear regression (R2 = 0.86, RMSE = 0.39). The neural network also acts conservatively in predicting this variable.

کلیدواژه‌ها English

Modulus of elasticity
Shear Wave Velocity
Limestone
Artificial neural network
Multivariate Regression
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