scholarly journals Prediction Models Based on Regression and Artificial Neural Network for Moduli of Layers Constituted by Open-Graded Aggregates

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1199
Author(s):  
Yunje Lee ◽  
Yongjin Choi ◽  
Donghyun Ahn ◽  
Jaehun Ahn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Area ◽  
Low Impact Development ◽  
Load Test ◽  
Engineering Properties ◽  
Rapid Urbanization ◽  
Permeable Pavement ◽  
Material Type ◽  
Artificial Neural

The impermeable cover in urban area has been growing due to rapid urbanization, which prevents stormwater from being naturally infiltrated into the ground. There is a higher chance of flooding in urban area covered with conventional concretes and asphalts. The permeable pavement is one of Low-Impact Development (LID) technologies that can reduce surface runoff and water pollution by allowing stormwater into pavement systems. Unlike traditional pavements, permeable pavement bases employ open-graded aggregates (OGAs) with highly uniform particle sizes. There is very little information on the engineering properties of compacted OGAs. In this study, the moduli of open-graded aggregates under various compaction energies are investigated based on the Plate Load Test (PLT) and Light-Weight Deflectometer (LWD). Artificial Neural Network (ANN) and Linear Regression (LR) models are employed for estimation of the moduli of the aggregates based on the material type and level of compaction. Overall, the moduli from PLT and LWD steeply increase until the number of roller passes reaches 4, and they gradually increase until the number of roller passes becomes 8. A set of simple linear equations are proposed to evaluate the moduli of open-graded aggregates from PLT and LWD based on the material type and the number of roller passes.

scholarly journals Towards building a neural network model for predicting pile static load test curves

2018 ◽  
Vol 149 ◽  
pp. 02031
Author(s):  
A. K. Alzo’ubi ◽  
Farid Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Static Load ◽  
Load Test ◽  
Static Load Test ◽  
Deep Foundation ◽  
Suggested Approach ◽  
Artificial Neural

In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

scholarly journals Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

2015 ◽  
Vol 19 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Ehsan Momeni ◽  
Ramli Nazir ◽  
Danial Jahed Armaghani ◽  
Harnedi Maizir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Back Propagation ◽  
Load Test ◽  
Static Load Test ◽  
Concrete Piles ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p class="MsoNormal" style="text-align: justify; line-height: 200%;">Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model.</p><p class="MsoNormal" style="text-align: justify; line-height: 200%;"> </p><p class="MsoNormal" style="text-align: justify; line-height: 200%;"><strong>Resumen</strong></p><p class="MsoNormal" style="text-align: justify; line-height: 200%;">La Capacidad Axial de Soporte (ABC, en inglés) de un pilote de construcción se determina usualmente a través de una Prueba de Carga Estática (SLT, inglés). Sin embargo, estas pruebas son costosas y demandan tiempo. La evaluación de las Dinámicas de Alto Esfuerzo de Pilotes (HSDPT, inglés), que la provee el programa de Análisis de Excavación (PDA, inglés), es una forma de aproximación más reciente para preveer la Capacidad Axial de Soporte. En comparación con la Prueba de Cargas Estática, la evaluación PDA es rápida y económica. La implementación de Redes Neuronales Arficiales (ANN, en inglés) que permita resolver problemas geotécnicos ha ganado atención recientemente debido a su posibilidad de hallar relaciones no lineales entre los diferentes parámetros. En este estudio se propone un modelo predictivo ANN para estimar la Capacidad Axial de Soporte de pilotes y su distribución. Para fines de una red de construcción se realizaron 36 pruebas PDA en pilotes de diferentes proyectos. Los resultados de los Análisis de Excavación, las características geométricas de los pilotes, al igual que los datos de investigación del suelo se utilizaron para probar los modelos ANN. Los resultados indican la viabilidad del modelo ANN en predecir la resistencia de los pilotes. Los coeficientes de correlación, R², que alcanzaron 0.941, 09.36 y 0.951 para la evaluación de los datos, revelan que la capacidad del pilotaje en el último rodamiento, en el cojinete del eje y en la punta que se predijeron con el modelo ANN concuerda con las establecidas a través del HSDPT. A través del análisis de respuesta se determinó que la longitud y el área de los pilotes son factores dominantes en el modelo predictivo propuesto.</p>

scholarly journals Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 720 ◽  
Author(s):  
Yuefeng Wang ◽  
Hossein Tabari ◽  
Youpeng Xu ◽  
Yu Xu ◽  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climate Variability ◽  
Water Level ◽  
Human Activities ◽  
Nonlinear Dependence ◽  
Rapid Urbanization ◽  
Evaluation Period ◽  
Water Level Changes ◽  
Artificial Neural

Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954–2014) were divided into baseline (1954–1987) and evaluation (1988–2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)).

scholarly journals Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

2020 ◽  
Vol 10 (5) ◽  
pp. 1871 ◽  
Author(s):  
Tuan Anh Pham ◽  
Hai-Bang Ly ◽  
Van Quan Tran ◽  
Loi Van Giap ◽  
Huong-Lan Thi Vu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Bearing Capacity ◽  
Mean Squared Error ◽  
Load Test ◽  
Coefficient Of Determination ◽  
Static Load Test ◽  
Foundation Design ◽  
Artificial Neural

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand

2015 ◽  
Vol 72 (3) ◽  
Author(s):  
Ramli Nazir ◽  
Ehsan Momeni ◽  
Kadir Marsono ◽  
Harnedi Maizir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bearing Capacity ◽  
Predictive Model ◽  
Linear Regression Analysis ◽  
Load Test ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Cohesionless Soils ◽  
Artificial Neural

This study highlights the application of Back-Propagation (BP) feed forward Artificial Neural Network (ANN) as a tool for predicting bearing capacity of spread foundations in cohesionless soils. For network construction, a database of 75 recorded cases of full-scale axial compression load test on spread foundations in cohesionless soils was compiled from literatures. The database presents information about footing length (L), footing width (B), embedded depth of the footing (Df), average vertical effective stress of the soil at B/2 below footing (s΄), friction angle of the soil (f) and the ultimate axial bearing capacity (Qu). The last parameter was set as the desired output in the ANN model, while the rest were used as input of the ANN predictive model of bearing capacity. The prediction performance of ANN model was compared to that of Multi-Linear Regression analysis. Findings show that the proposed ANN model is a suitable tool for predicting bearing capacity of spread foundations. Coefficient of determination R2 equals to 0.98, strongly indicates that the ANN model exhibits a high degree of accuracy in predicting the axial bearing capacity of spread foundation. Using sensitivity analysis, it is concluded that the geometrical properties of the spread foundations (B and L) are the most influential parameters in the proposed predictive model of Qu.

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