Prediction of elevation points using three different heuristic regression techniques

Demir, Vahdettin; DOĞU, Ramazan

doi:10.31127/tuje.1257847

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Şekil 3. Uygulama alanları (Demir & Doğu, 2022) Uygulama alanlarına göre eğitim aşaması sonuçları Tablo 2-4'te yer almaktadır.…”

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Estimation of Elevation Points Obtained by Remote Sensing Techniques by Different Artificial Neural Network Methods

DEMİR,

DOĞU

2023

TUZAL

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Bu çalışmanın amacı uzaktan algılama ve yapay sinir ağları yöntemleri kullanarak farklı büyüklüklerdeki alanlar için yükseklik tahmini gerçekleştirmektedir. Bu amaçla çok katmanlı yapay sinir ağları (ÇKYSA), radyal tabanlı yapay sinir ağları (RTYSA) ve genelleştirilmiş yapay sinir ağları (GRYSA) yöntemleri kullanılmıştır. Çalışmada yükseklik verileri Google-earth pro yazılımı kullanılarak elde edilmiştir. Çalışmada alanı olarak kentsel sınırlar dışında tahmini zor olabilecek dağlık bir bölge olan Ağrı Dağı ve yamaçları seçilmiştir. Çalışmada modeller eğitim ve test setlerine %80 ve %20 oranlarında ayrılmış ve üç farklı kriterine göre bulgular karşılaştırılmıştır. Bunlar karekök ortalama karesel hata, ortalama mutlak hata ve determinasyon katsayısı R2’dir. Çalışma sonuçları incelendiğinde en başarılı tahminlerin iki girişli (X ve Y) GRYSA kullanılarak elde edildiği ve yükseklik noktalarının tahmininde alternatif bir yöntem olarak kullanılabilineceği ortaya konmuştur.

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“…Şekil 3. Uygulama alanları (Demir & Doğu, 2022) Uygulama alanlarına göre eğitim aşaması sonuçları Tablo 2-4'te yer almaktadır.…”

Section: Bulgularunclassified

Estimation of Elevation Points Obtained by Remote Sensing Techniques by Different Artificial Neural Network Methods

DEMİR,

DOĞU

2023

TUZAL

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Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review

Terlumun,

Onyia,

Okafor

2024

AI Civ. Eng.

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Concrete is one of the most common construction materials used all over the world. Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation. However, researchers have increasingly turned to predictive models to streamline this process. This review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence (AI) techniques. Self-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement complexities. This review evaluates various AI techniques through a comparative performance analysis. The findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive accuracy. Specifically, artificial neural network (ANN) models exhibit robustness, consistently achieving R2 values exceeding 0.7 across reviewed studies, thereby demonstrating their efficacy in predicting concrete compressive strength. The integration of ANN models is recommended for formulating various civil engineering properties requiring predictive capabilities. Notably, the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing, which can otherwise delay construction activities.

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Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks

Uzer

2024

Turkish Journal of Engineering

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In recent years, artificial neural networks (ANNs) have emerged as highly effective tools for addressing the intricate challenges encountered in geotechnical engineering. ANNs find application in a variety of geotechnical problems, showcasing promising outcomes. This study aims to improve the efficiency of predicting intermediate values from unconfined compressive strength (UCS) data obtained from laboratory tests through the use of ANNs. The modelling of artificial neural networks was carried out using the Regression Learner program, integrated with the Matlab 2023a software package, offering a user-friendly graphical interface for AI model development without the need for coding. The ANNs' validation and training were based on UCS test data obtained from the Geotechnical Laboratory of Iowa State University. These laboratory tests focused on engineering properties, specifically the UCS) of soils treated with biofuel co-products (BCPs). The dataset, organized in a matrix of size 216 × 5, features columns providing information on soil type (Soil 1; Soil 2; Soil 3; Soil 4), sample type (pure soil-unadulterated; 12% BCP-adulterated soil; 3% cement; 6% cement; 12% cement adulterated soil), time (1, 7, and 28 days), moisture content (OMC-4%, OMC%, and OMC+4%), and corresponding UCS peak stress (psi) values. The AI predictions for the test data output achieved an outstanding R2 score of 0.93, showcasing the potential of employing ANNs to efficiently acquire a substantial amount of data with fewer experiments and in less time. This approach holds promise for applications in geotechnical engineering.

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Prediction of elevation points using three different heuristic regression techniques

Cited by 4 publications

References 28 publications

Estimation of Elevation Points Obtained by Remote Sensing Techniques by Different Artificial Neural Network Methods

Estimation of Elevation Points Obtained by Remote Sensing Techniques by Different Artificial Neural Network Methods

Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review

Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks

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