A Fuzzy‐Neural Network Method for Modeling Uncertainties in Soil‐Structure Interaction Problems

Provenzano, P.

doi:10.1111/1467-8667.00326

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…İlk kez Zadeh [16] tarafından önerilen bulanık küme kuramı, karmaşık ve tanımlanmasında güçlüklerin yaşandığı sistemlerin modellenmesinde esnek ve tutarlı bir şekilde uygulanabilmektedir [17]. Bulanık küme kuramı mühendislik jeolojisi uygulamalarında birçok araştırmacı tarafından kullanılmıştır [18]- [23]. Ayrıca, değişik zemin sınıflama sistemlerindeki belirsizliklerin azaltılması amacıyla kullanıldığı çalışmalar da mevcuttur.…”

Section: Bulanıklaştırma Süreciunclassified

An application of a fuzzy classification routine suggested for fine grained soils

Toksöz¹,

Yilmaz²

2020

Pamukkale J Eng Sci

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Mühendislik jeolojisinde tasarıma yönelik ön çalışmalarda zeminlerin sınıflandırılması önemli bir aşamadır. Bir zemine atanabilecek birkaç harf ya da sayı uygulayıcı mühendislere çok hızlı olarak bir fikir sağlamaktadır. Ulusal ve uluslararası zemin sınıflama sistemlerinde ince taneli zeminlerin sınıflandırılması plastisite abağı ile yapılmaktadır. Ancak, likit limit ve plastik limit deneylerini etkileyen bazı faktörlere bağlı olarak zemin sınıfının plastisite abağı ile belirlenmesi sırasında belirsizlikler oluşmaktadır. Ayrıca, tanımlama noktalarının kimi zaman sınıfları ayıran çizgiler üzerine veya yakınına düşmesi durumunda belirsizlik seviyesi artmaktadır. Bu çalışmada söz konusu belirsizlikleri en aza indirmek için daha önce geliştirilen bir bulanık sınıflama sürecinin uygulaması yapılmıştır. Söz konusu bulanık sınıflama sürecinde plastisite abağındaki tanımlama noktalarının sınırları ayıran çizgilere olan dik mekansal mesafeleri dikkate alınmıştır. Sigmoid fonk † siyonu kullanılarak tanımlama noktalarının bulanık plastisite ve bulanık zemin değerleri hesaplanmıştır. Son olarak bulanık işlemciler yardımı ile bulanık zemin sınıfları elde edilmiştir. Önerilen bulanık sınıflama süreci 100 adet laboratuvar verisi üzerinde uygulanmış ve elde edilen bulanık zemin sınıfları klasik zemin sınıfları ile karşılaştırılmıştır. Soil classification is an important stage in preliminary studies for design applications in engineering geology. A few letters or numbers assigned to a soil provide rapidly an idea to engineers. The classification of fine grained soils in national and international soil classification systems is determined using plasticity chart. However, because of the factors affecting liquid and plastic limit tests, uncertainties can arise in classification of fine-grained soils when this approach is used. Furthermore, uncertainty level has increased in case points fall sometimes on the lines or near the lines. In this study, an application of a fuzzy classification routine which was suggested before to minimize these uncertainties was performed. In the suggested method, the spatial distances of the evaluation points on the chart from the lines were considered. The membership degrees that define the fuzzified soil and plasticity were determined by using sigmoid function. Finally, fuzzy soil classes were obtained by using fuzzy operators. The suggested fuzzy classification routine, was performed on classification of 100 fine-grained soil samples ad obtained fuzzy soil classes were compared with crisp soil classes.

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Section: Bulanıklaştırma Süreciunclassified

An application of a fuzzy classification routine suggested for fine grained soils

Toksöz¹,

Yilmaz²

2020

Pamukkale J Eng Sci

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“…In recent years, the research has largely investigated the application of new computational techniques and mathematical theories to the engineering practice in designing structures (Adeli and Kumar, 1999; Saltan et al, 2002; Provenzano, 2003; Lee et al, 2011); optimizing structure costs (Sarma and Adeli, 2000a, 2000b, 2002; Adeli and Sarma, 2006; Li et al, 2010); and building structural stability (Marano et al, 2011), traffic incident detections (Adeli and Karim, 2000; Samant and Adeli, 2001; Karim and Adeli, 2002), railroad track management (Peng et al, 2011), and pavement management (e.g., Banan and Hjelmstad, 1996; Holquin‐Veras, 1997; Roberts and Attoh‐Okine, 1998; Yang et al, 2003; Loizos and Karlaftis, 2006; Bianchini and Bandini, 2010; Lajnef et al, 2011; Wang and Li, 2011). Specifically for this study, the literature included many fuzzy logic applications to pavement condition models.…”

Section: Pavement Condition Modelsmentioning

confidence: 99%

Fuzzy Representation of Pavement Condition for Efficient Pavement Management

Bianchini

2012

Computer aided Civil Eng

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Many government agencies and private consulting companies manage large pavement networks in terms of infrastructure condition assessment and maintenance planning. Efficient pavement management is supported by pavement management systems (PMSs), which includes models for pavement condition assessments considered "valuable" by agency's engineers. The objective of this article is to define a pavement condition model able to overcome surveyors' subjectivity in rating distresses and thus provide meaningful pavement conditions for the agencies to employ in project planning. The article proposes a fuzzy inference model for calculating pavement condition ratio (PCR) specifically tailored on the Alabama Department of Transportation Pavement (ALDOT) guidelines and policies. Applied to several surveyors' ratings, the proposed model has the ability to smooth distress extent differences among surveyors producing PCR values within acceptable range of variability. The proposed approach has the intention of not only enhancing pavement condition characterization but also to exploit the opportunity made available by automation in the collection and interpretation of pavement data which are anyway characterized by an inherent subjectivity. C 2012 Computer-Aided Civil and Infrastructure Engineering.

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“…The training set contained two‐thirds of the initial set whereas the validation set was composed of the remaining one‐third of the data. The criteria applied in the determination of the two sets were derived from the literature review of the theory and applications of neural networks (e.g., Cammarata, 1997; Kasabov, 1998; Haykin, 1999; Provenzano, 2003). Step 2 : Clustering.…”

Section: Construction and Characteristics Of The Neuro‐fuzzy Modelmentioning

confidence: 99%

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

Bianchini

Bandini

2010

Computer-Aided Civil and Infrastructure Engineering

116

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Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro‐fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF‐THEN fuzzy rules. The results of the neuro‐fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro‐fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.

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A Fuzzy‐Neural Network Method for Modeling Uncertainties in Soil‐Structure Interaction Problems

Cited by 19 publications

References 15 publications

An application of a fuzzy classification routine suggested for fine grained soils

An application of a fuzzy classification routine suggested for fine grained soils

Fuzzy Representation of Pavement Condition for Efficient Pavement Management

Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

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