Prediction of soil-bearing capacity on forest roads by statistical approaches

Varol, Tuğrul; Özel, Halil Barış; Ertuğrul, Mertol; Emir, Tuna; Tunay, Metin; Çetin, Mehmet; Şevik, Hakan

doi:10.1007/s10661-021-09335-0

Cited by 35 publications

(13 citation statements)

References 30 publications

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“…The artificial neural network (ANN) in recent times has become very useful for pattern recognition, grouping, clustering and prediction in many fields. The ANN is a type of model used in machine learning (ML) and has emerged as a veritable substitute to known regression and statistical models in use and efficiency Varol et al [16]. The ANN implementation and ANN training and prediction quality are the two final analysis of the system [17,18].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash

Nnochiri¹,

Okokpujie²,

Tartibu³

2023

RIA

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California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repeated carrying out of this test has been chiefly monotonous and time wasting, also the use of cement as stabilizer has also been increasingly expensive, hence, the need for admixing with agrowaste ash such as rice husk ash (RHA). This research is carried out for the prediction of the CBR of lateritic soil admixed with cement and RHA by means of an artificial neural network (ANN). Six parameters are selected as input variables to obtain results that are accurate and precise. The six input variables are cement, RHA, liquid limit, plasticity index, maximum dry density and optimum moisture content, while CBR Unsoaked and CBR Soaked are the output variables. The study consists of a database of 1288 samples obtained from laboratory experiments which were subdivided into 70% for training, 15% for testing, and 15% for validation. The training operation is performed by a multilayer perceptron-back propagation algorithm. The network topology is achieved after fixing a number of hidden neurons. Thereafter, statistical indices are used in evaluating the performance of the ANN model. It is established that this model is appropriate for accurate prediction of CBR results.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash

Nnochiri¹,

Okokpujie²,

Tartibu³

2023

RIA

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“…Qualitative methods are evaluated in the absence of historical event data, mainly based on the experience, judgment, and opinions of experts, such as terrain analysis and expert scoring methods; while quantitative methods have sufficient data in historical event collection and a slope stability evaluation model is established. The slope stability analysis methods can be summarized as: fuzzy method (Leonardi et al 2020), vulnerability analysis (Zhang et al 2020), logistic regression (Das et al 2012;Shao et al 2020;Sun et al 2020;Khalaj et al 2020), finite element method (Pradhan and Siddique 2020), risk analysis (Lin et al 2017(Lin et al , 2020, linear discriminant analysis (Nepal et al 2019), regression analysis (Varol et al 2019), differential InSAR (Nappo et al 2019, artificial neural networks (Choobbasti et al 2009;Tsangaratos and Benardos 2013;Varol et al 2021), and adaptive network-based fuzzy inference system methods (Varol et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessment of potential sediment disasters and resilience management of mountain roads using environmental indicators

Lin

Lai

et al. 2022

Nat Hazards

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In recent years, extreme rainfall events occur frequently, causing serious watershed sediment disasters, destroying mountain roads, and endangering the safety of residents' lives and property. This study aims to deal with the spatial change of potential sediment movement on the road slope pre-disaster and to screen disaster hot spots for early warning and control system. The conceptual model is used to simulate the distribution of primary and/or derived disasters on a watershed scale to assess the impact of sediment disasters caused by heavy rain event. Correlation analysis shows that the models in assessment of primary disaster and derived disaster are significantly correlated with the collapse ratio and disaster ratio, respectively. Since the primary disaster has been considered when calculating the derived disaster risk, the terrain subdivision along Provincial Highway 21 (Tai-21) is extracted to understand the derived sediment disaster on the road slope. The model can effectively evaluate the road sections prone to disasters. According to the risk level, the hot spot of road slope disasters and the management of disaster resilience are determined and can be the reference for disaster prevention and control.

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“…The most dominant environmental factors affecting plant growth are climatic [11][12][13][14] and edaphic situations [15][16][17]. Perhaps the most important of these factors is nutritional elements.…”

Section: Introductionmentioning

confidence: 99%

Changes of Plant Nutrients K and Mg in Several Plants Based on Traffic Density and Organs

Koç

Canturk

ÇOBANOĞLU

2022

KUJES

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This study aimed to determine the variations in K and Mg element concentrations in branches, leaves, and seeds of Aesculus hippocastanum, Prunus ceracifera, Tilia platyphyllos, Acer negundo, and Ailanthus altissima, which are frequently used in urban areas, especially in landscape studies, based on traffic density. As a result, the changes in the elements based on the traffic density in all organs and organs in all traffic intensities were significant (P

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Prediction of soil-bearing capacity on forest roads by statistical approaches

Cited by 35 publications

References 30 publications

Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash

Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash

Assessment of potential sediment disasters and resilience management of mountain roads using environmental indicators

Changes of Plant Nutrients K and Mg in Several Plants Based on Traffic Density and Organs

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