استفاده از فناوری AI و مدل ENN / t-SNE برای ارزیابی روانگرایی خاک
ترجمه نشده

استفاده از فناوری AI و مدل ENN / t-SNE برای ارزیابی روانگرایی خاک

عنوان فارسی مقاله: ارزیابی روانگرایی خاک با استفاده از ترکیب فناوری AI و مدل کوپل ENN / t-SNE
عنوان انگلیسی مقاله: Evaluation of soil liquefaction using AI technology incorporating a coupled ENN / t-SNE model
مجله/کنفرانس: مهندسی زلزله و دینامیک خاک - Soil Dynamics And Earthquake Engineering
رشته های تحصیلی مرتبط: عمران
گرایش های تحصیلی مرتبط: زلزله، مدیریت ساخت، خاک و پی
کلمات کلیدی فارسی: تکامل تفاضلی، بهینه سازی، t-SNE، شبکه عصبی، روانگرایی
کلمات کلیدی انگلیسی: Differential evolution، Optimisation، t-SNE، Neural network، Liquefaction
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.soildyn.2019.105988
دانشگاه: Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang District, Shanghai, 200240, China
صفحات مقاله انگلیسی: 10
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 2/989 در سال 2019
شاخص H_index: 78 در سال 2020
شاخص SJR: 1/359 در سال 2019
شناسه ISSN: 0267-7261
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14297
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Brief review of existing methods

3- Methodological approach

4- Case study: liquefaction potential assessment

5- Conclusions

References

بخشی از مقاله (انگلیسی)

Abstract

This paper presents a new evolutionary neural network (ENN) algorithm coupled with the dimensionality reduction technique ‘t-distributed stochastic neighbour embedding’ (t-SNE). The ENN model features the crossbreeding of a differential evolution method and a stochastic gradient optimisation algorithm. The t-SNE is used to visualise the training and testing datasets and the ENN model performance. The proposed ENN model is applied to a relatively large soil liquefaction database. The good convergence and generalisation ability of the proposed model and the negligible misclassification results demonstrate that the proposed ENN model can provide accurate, efficient, and flexible results. The prominent and practical abilities of t-SNE to recover the structure of the initial conditions and to demonstrate the ENN model performance are discussed. This coupled approach simplifies the analysis and/or prediction of hazards for which large quantities of data are required.

Introduction

One of the focuses of civil engineers in the past three decades has been to tackle the soil liquefaction phenomenon efficiently, given the random nature of its occurrence. Soil liquefaction occurs when a saturated or partially saturated soil substantially loses strength and stiffness in response to an applied stress (such as shaking during an earthquake or other sudden changes in its stress condition), in which a material that is ordinarily a solid behaves like a liquid [1,2]. Soil liquefaction occurs when the effective stress (shear strength) of the soil is reduced to essentially zero and imposes a real risk on the surrounding structures [3–8]. The dedication of engineers and researchers has thus been triggered and further reinforced by a plethora of casualties [9] and the severity of structural damages [10–13] caused by this phenomenon. However, the prediction of soil liquefaction has remained a formidable task owing to the nonlinearity of soil behaviour [14–22] and the characteristics of the seismic power dissipation. The traditional methods for predicting soil liquefaction [23–26] still have numerous limitations, which have initiated the proposals of more powerful (efficient, flexible, and accurate) strategies. Among them, data mining (DM) has attracted particular attention recently. Data mining (DM) is a multidisciplinary subfield of computer science that includes statistics, database technology, and machine learning for the analysis of previously unknown or unsuspected relationships buried in large datasets. The DM algorithms are increasingly accepted in geotechnical engineering and have generally shown an excellent ability to solve multi-dimensional and complex problems [27].