Predicting compressive strength using the texture coefficient with soft computing techniques for rocks

Abstract

Rock strength plays one of the most dominant roles for mining, geology, and civil engineering in terms of planning, excavation, and safety. Compressive strength (fc), which is the most used strength type, requires time, cost, and standard size specimens are needed to find it in the laboratory. In this study, Regression Analysis (RA), Neural Networks (NNs), Gene-Expression Programming (GEP), and Adaptive Network-based Fuzzy Inference System (ANFIS) were used for predicting using both textural and mechanical properties which are detected with a dimensionless sample or directly in the field. For this purpose, a data set consists of 136 data value (46 magmatic, 77 sedimentary and 13 metamorphic rocks) was used, and three different feature sets were constructed. The comparison of the estimated results with each other was performed by training, testing, and checking of these models. The comparisons and results of the statistical analyses indicate that soft computing techniques represent significantly effective methods to calculate fc even in situations when input and output values are not related to each other, and it is possible to create statistically suitable and valid mathematical models by everyone using GEP.

Keywords

• Texture coefficient
• Compression strength
• Adaptive network-based fuzzy inference system
• Neural networks
• Gene-expression programming.

Kayalar için yapay zekâ hesaplama teknikleri ile doku katsayını kullanarak basınç dayanımını tahmin etme

Öz

Kaya dayanımı, planlama, kazı ve güvenlik açısından madencilik, jeoloji ve inşaat mühendisliği için en baskın rollerden birini oynar. En çok kullanılan dayanım türü olan basınç dayanımını (fc), laboratuvar şartlarında bulmak için zaman, maliyet ve standart boyutlu numunelere ihtiyaç vardır. Bu çalışmada, kayaların şekilsiz numuneler üzerinde veya araziden elde edilen hem doku katsayıları hem de basınç dayanım değerleri regresyon analizi (RA), Sinir Ağları (NN'ler), Gen- ekspresyonu Programlama (GEP) ve Uyarlanabilir Ağ Tabanlı Bulanık Mantık Sistemi (ANFIS) kullanılarak tahmin edilmiştir. Bu amaçla 136 veriden oluşan bir veri seti (46 magmatik, 77 tortul ve 13 metamorfik kayaç) kullanılmış ve üç farklı özellik seti oluşturulmuştur. Tahmin edilen sonuçların birbirleri ile karşılaştırılması bu modellerin eğitimi, test edilmesi ve kontrol edilmesi ile yapılmıştır. İstatistiksel analizlerin karşılaştırmaları ve sonuçları, yapay zekâ hesaplama tekniklerinin, girdi ve çıktı değerlerinin birbiriyle ilişkili olmadığı durumlarda bile fc'yi hesaplamak için önemli ölçüde etkili olduğunu ve istatistiksel olarak uygun ve geçerli matematiksel modeller oluşturmanın GEP kullanan herkes tarafından yapılmasının mümkün olduğunu göstermektedir.

Anahtar Kelimeler

• Doku katsayısı
• basınç dayanımı
• Adaptif ağ tabanlı bulanık mantık sistemi
• Sinir ağları
• Gen ekspresyonu programlama.

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