Destek vektör regresyonu yaklaşımı ile istinat duvarı tasarım parametrelerinin belirlenmesi

Öz

Bir istinat duvarında kritik kayma yüzeyinin yeri (αcr), sismik aktif toprak itkisinin büyüklüğü (Pae) ve etki mesafesi (zae); arka dolgu zeminin özellikleri, yükleme durumu ve problem geometrisi ile alakalı birçok parametreden etkilenmektedir. Son yıllarda, bunun gibi çok sayıda girdi ve çıktı değişkenine sahip mühendislik problemlerinde, Destek Vektör (Makine) Regresyonu (DVR) gibi güçlü öğrenme algoritmalarının parametreler arasındaki regresyon ilişkilerini ortaya çıkarılmasında kullanılmasıyla kompleks hesap adımlarını izlemeden bilinmesi gereken tasarım parametrelerinin tahmini üzerinde durulmaktadır. Bu çalışmada DVR ile, parametrik olarak üretilen 119393 veri içeren büyük hacimli bir data kümesinden 11 adet girdi parametresi ve 3 adet çıktı parametresi arasındaki en iyi regresyon ilişkileri, 2 farklı örnekleme tekniği, 6 değişik çekirdek fonksiyonunun kullanılmasıyla ile ortaya çıkarılmıştır. Pae için kübik fonksiyon, αcr ve zae için medium gauss fonksiyon en iyi DVR modellerini oluşturmuşlardır. Model tahminlerinin gerçek değerden sapmaları sırasıyla ±20 kN/m2, ±8º ve ±0,15 m gibi bir değişim bandı içinde yer almıştır. Bununla birlikte örnekleme yöntemlerinin modellerin öngörü performansı üzerinde etkisi hemen hemen hiç olmamıştır. DVR modellerin doğrulanması, 4374 adet veri barındıran yeni bir data seti ile literatürdeki başka bir çalışmanın sonuçları ile istatistiksel olarak karşılaştırılması suretiyle gerçekleştirilmiştir. Burada, DVR modeller en başarılı tahmin performansını Pae ve αcr için sergilemişlerdir. Bununla birlikte zae için yapılan DVR öngörülerinin göreceli olarak bir parça zayıf kaldığı gözlense de halen istatistiksel olarak kabul edilebilir seviyededir.

Anahtar Kelimeler

• Destek vektör (makine) regresyonu
• İstinat duvarlarının tasarım parametreleri
• Kohezyonlu arka dolgu zemini
• İstatistiksel göstergeler
• Sismik yükler
• , Sismik yükler

Estimation of design parameters of a retaining wall with support vector regression approach

Öz

The location of critical sliding surface (αcr), magnitude (Pae) and application point (zae) of seismic active earth thrust are influenced by many parameters related to the soil properties of backfill, the loading conditions and the cross-section geometry of problem for a retaining wall. In recent years, the application of powerful learning algorithms such as Support Vector (Machine) Regression (SVR), to reveal the regression relationships within a large number of input and output variables for engineering problems such as this, has been presented on the estimation of the design factors that should be known without following the complex and complicated calculation steps. In this study, the SVR approach considering 6 different kernel functions with 2 data sampling techniques is applied to find out the best regression relationships between 11 inputs and 3 outputs from a parametrically generated big data set containing 119393 data. Cubic kernel function for Pae and medium gauss kernel function for αcr and zae achieve the best predictive SVR models. The deviations between the predicted and actual values are estimated within a range of ± 20 kN/m2, ± 8º and ± 0.15 m, respectively. Besides, the two types of sampling methods have almost no statistical and practical effects on the performance of the models. The verification of the SVR models is also carried out by statistically comparing the results of another research in the literature using a totally new data set containing 4374 data. Herein, the most successful predictive performance of the SVR models presents for Pae and αcr. However, it is observed that the SVR predictions for zae are relatively weaker but still statistically at an acceptable level.

Anahtar Kelimeler

• Support Vector (Machine) Regression
• Design parameters of retaining walls
• Seismic loads
• Cohesive backfill soil
• Statistical indicators

Kaynakça

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