TAM DERİNLİKLİ ESNEK ÜSTYAPILARIN KATMAN ÖZELLİKLERİNİN TAHMİNİ İÇİN LİG ŞAMPİYONASI ALGORİTMASI

Year 2020, Volume: 8 Issue: 1, 273 - 284, 20.03.2020

Onur Pekcan

https://doi.org/10.21923/jesd.693743

Abstract

Bu çalışma tam derinlikli esnek üstyapıların katman özelliklerinin tahmin edilmesinde kullanılacak, Lig Şampiyonası Algoritması (LCA) ve Yapay Sinir Ağları (ANN) tabanlı bir geri hesaplama algoritması önermektedir. LCA-ANN adı verilen bu algoritma iki ana bölümden oluşmaktadır: (i) yol yüzeyindeki deplasmanların hesaplandığı, lineer olmayan sonlu elemanlar çözümleri ile geliştirilen, ANN ileri hesaplama modeli ve (ii) ANN modeline girdi olarak verilecek en uygun katman elastisite modullerinin belirlenmesinde kullanınan LCA arama algoritmasıdır. Önerilen yönetimin performansını değerlendirmek amacıyla sentetik olarak üretilen veri seti ile gerçek bir veri seti kullanılmıştır. Ayrıca, LCA’nın arama yeteğini değerlendirmek için, kabul görmüş algoritmalar olan Basit Genetik Algoritma (SGA) ve Parçacık Sürü Optimizasyonu (PSO) karşılaştırma amacıyla kullanılmıştır. Elde edilen sonuçlar göstermiştir ki önerilen algoritma düşük hata miktarlarıyla esnek üstyapı katman özellikleri tahmin edebilmekte ve geri hesaplama çalışmalarında hızlı ve güvenilir bir yöntem olarak ortaya çıkmaktadır. 

Keywords

Esnek Yol Kaplaması, Geri Hesaplama, Lig Şampiyonası Algoritması, Yapay Sinir Ağları

LEAGUE CHAMPIONSHIP ALGORITHM FOR LAYER MODULI ESTIMATION OF FULL-DEPTH ASPHALT PAVEMENTS

Abstract

This study proposes a backcalculation tool, based on the hybrid use of League Championship Algorithm (LCA) and Artificial Neural Network (ANN), in order to predict the stiffness related layer properties of full-depth asphalt pavements. The proposed algorithm, namely LCA-ANN, is composed of two main parts; (i) an ANN forward response model, which is developed with the nonlinear finite element solution, for computing the surface deflections, and (ii) LCA search algorithm which is employed to search and provide the best set of layer moduli to the ANN model. In order to evaluate the performance of the proposed method, a synthetically generated dataset and real field data are utilized. Moreover, to assess the searching ability of LCA, well-accepted metaheuristic algorithms; Simple Genetic Algorithm (SGA) and Particle Swarm Optimization (PSO) are employed for comparison purposes. Obtained results reveal that the proposed algorithm can predict the layer properties with a low order of error values and enables fast and reliable tool for backcalculation studies.

Keywords

Flexible Pavements, Backcalculation, League Championship Algorithm, Artificial Neural Network

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