Geoteknik mühendisliğinde yapay sinir ağı uygulamaları ve bir örnek: Zemin profilinin tahmin edilmesi

Abstract

   The use of artificial neural networks (ANN) in geotechnical engineering has gained wide application in Turkey as well as the world during the past ten years. A comprehensive literature survey has shown that applications are concentrated in basic areas such as classification, site characterization, liquefaction, hydraulic conductivity, compaction, consolidation as well as practice comprising the problems of retaining structures, settlement of foundations, pile capacity and modeling of soil behavior where the relationship among the several parameters involved is not always thoroughly understood. Several case histories are presented as examples. Latest research shows that artificial neural networks are heading towards unison with fuzzy logic and genetic algorithms and it is certainly superior to the statistical methods.The second part of the paper gives an account of research conducted into estimation of the soil profiles in the city of Adapazari, Turkey. There has been widespread damage and destruction in the city during the Mw=7.4 earthquake in 1999. The damage has largely been blamed on inferior alluvial deposits and parts of the city have been moved to the North where the soil was found to be "sound". The sediments in the city are the products of the meandering river Sakarya which also flooded the region almost biannually. The two processes have resulted in the formation of complex soil profiles and near chaotic profiles are frequent. The silty layers are possibly the source of ground failures, occasionally leading to liquefaction in the absence of sands. A comprehensive soil investigation has been carried out since 1990 by boreholes and cone penetration soundings. The authors have used the rich database available established from previous and current laboratory and field investigations. Out of this voluminous data those pertaining to depths of 2 to 7m have been used for the ANN work, as those depths have been diagnosed as the possible liquefaction zone. Data from 117 CPT sites whose coordinates were known were employed for this study. The 3236 readings of tip resistance and sleeve friction were used to establish the ANN model. The well established Robertson classification chart defines nine types of soil. It requires the normalised values of tip resistance(Qt) and and sleeve friction(Fr) a identify the soil layer. The Qt* and Fr* are further defined to form the spatial distribution by the use of equations. The training matrix even for a limited depth of 5m using the data from 90 CPTU tests came out to be of size 3236x3236, which was difficult to handle. Consequently, thirteen 1800 by 1800 matrices were established (1.60-1.98m, 2.00-2.38m, 2.40-2.78m, 2.80-3.18m, 3.20-3.58m, 3.60-3.98m, 4.00-4.38m, 4.40-4.78m, 4.80-5.18m, 5.20-5.58m, 6.00-6.38m, 6.40-6.78m, 6.80-6.98m). Data from 27 CPTU were used to form the thirteen 540 by 1800 simulation matrices and 1800 by 1800 training matrices. Analyses were carried out on the Matlab 2010a Toolbox7 NNtraintool interface. 60% of the data were employed for Training, 15% for Validation and 15% for Testing. Inspecting the results, it was found that the success rate in estimating the soil profile anywhere in the 26 km2 city area was as high as 92%. This is a surprisingly high success rate considering the highly complex and laterally variable soil profiles throughout the city.  Keywords: Geotechnical engineering, artificial neural network, artificial intelligence, soil profile, cone penetration test, site characterization.Dünyada ve Türkiye’de yapay sinir ağlarının (YSA) geoteknik mühendisliğinin pek çok alanında kullanımı son yıllarda yaygınlaşmıştır. Literatür araştırmasında; geoteknik probleminin çözümünde kullanılan YSA nın özellikle; zeminlerin sınıflandırılması, arazi karakterizasyonu, sıvılaşma, geçirimlilik ve hidrolik iletkenlik, sıkıştırma, dayanma yapıları, temellerin oturması, kazık hizmet yükünün tahmini ve zemin davranışının modellenmesi gibi karmaşık ve ilişkinin iyi anlaşılamadığı pek çok doğrusal olmayan problemlerin çözümünde başarılı ve hızlı çözümler sağladığı görülmektedir. Yapılan son çalışmalar, YSA’nın bulanık mantık ve genetik algoritma ile bütünleştiğini göstermektedir. Bu makalenin konusunu, geoteknik mühendisliğinin çeşitli dallarındaki problemlerin çözümünde YSA uygulamalarının genel bir değerlendirmesi oluşturmaktadır. Bu kapsamda YSA modellenmesi ve bu konuda yapılmış yurtdışından ve ülkemizden bazı çalışmalara örnekler verilmiştir. Makalenin ikinci bölümünde yazarlarca geliştirilen Adapazarı zeminlerinde 2-7m arası zemin profilinin CPT verileriyle analizinde yapay sinir ağının kullanımına yer verilmiştir. Koordinatları bilinen toplam 117 adet Koni Penetrasyon (CPT), verilerinden oluşturulan veri tabanı ile her 2cm. de bir alınan toplam 3236 okumayla geliştirilmiş yapay sinir ağı modeli ile rastgele seçilen lokasyonlar için yapılan tahminlerde %92 gibi oldukça yüksek bir başarı elde edilerek arazi karakterizasyonu hakkında yorum yapılabileceği görülmüştür. Anahtar Kelimeler: Geoteknik mühendisliği, yapay sinir ağları, yapay zeka, zemin profili, koni penetrasyon deneyi, arazi karakterizasyonu.&nbsp