scholarly journals Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

2019 ◽  
Vol 9 (21) ◽  
pp. 4594 ◽  
Author(s):  
Hossein Moayedi ◽  
Bahareh Kalantar ◽  
Anastasios Dounis ◽  
Dieu Tien Bui ◽  
Loke Kok Foong
Keyword(s):  
Bearing Capacity ◽  
Prediction Models ◽  
Soil Layer ◽  
Ultimate Bearing Capacity ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Fe Modelling ◽  
Ann Models ◽  
Circular Footing

In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

Estimating bearing capacity of shallow foundations by artificial neural networks

2017 ◽  
Vol 3 (4) ◽  
pp. 151
Author(s):  
Mustafa Aytekin
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Large Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Capacity Data

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

scholarly journals Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan-Ting Huang ◽  
Choo-Aun Neoh ◽  
Shun-Yuan Lin ◽  
Hon-Yi Shi
Keyword(s):  
Pain Control ◽  
Prediction Models ◽  
Myofascial Pain ◽  
Training Dataset ◽  
Percentage Error ◽  
Support Vector ◽  
Ann Model ◽  
Dry Needling ◽  
Patient Reported ◽  
Ann Models

Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR).Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at baseline and at 1 year postoperatively.Results. Compared to the MLR and SVM models, the ANN model generally had smaller mean square error (MSE) and mean absolute percentage error (MAPE) values in the training dataset and testing dataset. Most ANN models had MAPE values ranging from 3.4% to 4.6% and most had high prediction accuracy. The global sensitivity analysis also showed that pretreatment BPI score was the best parameter for predicting pain after dry needling.Conclusion. Compared with the MLR and SVM models, the ANN model in this study was more accurate in predicting patient-reported BPI scores and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Development of Pavement Distress Deterioration Prediction Models for Urban Road Network Using Genetic Programming

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Tanuj Chopra ◽  
Manoranjan Parida ◽  
Naveen Kwatra ◽  
Palika Chopra
Keyword(s):  
Genetic Programming ◽  
Road Network ◽  
Prediction Models ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Validation Dataset ◽  
Urban Road Network ◽  
Pavement Distress ◽  
Urban Road ◽  
Progression Model

The objective of the present study is to develop models to predict the deterioration of pavement distress of the urban road network. Genetic programming (GP) has been used to develop five models for the prediction of pavement distress: Model 1 for the cracking progression, Model 2 for the ravelling progression, Model 3 for the pothole progression, Model 4 for the rutting progression, and Model 5 for the roughness progression. The data have been collected from the roads of Patiala City, Punjab, India; during the years 2012–2015, the network of 16 roads have been selected for the data collection purposes. The data have been divided into two sets, that is, training dataset (data collected during the years 2012 and 2013) and validation dataset (data collected during the years 2014 and 2015). The two fitness functions have been used for the evaluation of the models, that is, coefficient of determination (R2) and root mean square error (RMSE), and it is inferred that GP models predict with high accuracy for pavement distress and help the decision makers for adequate and timely fund allocations for preservation of the urban road network.

scholarly journals Reliability prediction of the axial ultimate bearing capacity of piles: A hierarchical Bayesian method

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881105
Author(s):  
Shengliang Lu ◽  
Jie Zhang ◽  
Shirong Zhou ◽  
Ancha Xu
Keyword(s):  
Bearing Capacity ◽  
Soil Layer ◽  
Ultimate Bearing Capacity ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Land Resources ◽  
Proposed Model ◽  
The Mean ◽  
Sea Reclamation ◽  
Hierarchical Bayesian Method

The sea reclamation is one of the efficient ways to alleviate the shortage of land resources due to population growth, and the corresponding axial ultimate bearing capacity of piles has become one of the critical factors for evaluating the performance of the soil layer reclamation work. Many models are used to analyze the testing data. However, these models cannot describe the mean population bearing capacity and unit-to-unit variation simultaneously, and they cannot give the reliability of predicting the axial ultimate bearing capacity of piles. Thus, they are rarely used in practice. In this article, we propose a mixed-effects model, which could overcome the drawback of the models in the literature. A hierarchical Bayesian framework is developed to estimate the model parameters using Gibbs sampling. The proposed model is applied to a real pile dataset collected in silt-rock layer area, and we predict the mean axial bearing capacities under different reliability levels.

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

2015 ◽  
Vol 28 (2) ◽  
pp. 391-405 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Raja Shahrom Nizam Shah Bin Raja Shoib ◽  
Koohyar Faizi ◽  
Ahmad Safuan A. Rashid
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Ann Model

scholarly journals Bearing Capacity of Recycled Self-Compacting Concrete-Filled Circular Steel Tubular Long Columns Subjected to Axial Load

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Feng Yu ◽  
Jie Song ◽  
Shuangshuang Bu ◽  
Jingfeng Wang ◽  
Haiying Wan ◽  
...  
Keyword(s):  
Bearing Capacity ◽  
Axial Load ◽  
Prediction Models ◽  
Slenderness Ratio ◽  
Ultimate Bearing Capacity ◽  
Self Compacting Concrete ◽  
Strength Grade ◽  
Short Columns ◽  
Calculation Results ◽  
Buckling Failure

To investigate the ultimate bearing capacity and deformation of the recycled self-compacting concrete-filled circular steel tubular (RSCCFCST) long columns subjected to axial load, nine specimens with different recycled self-compacting concrete (RSCC) strength grades and slenderness ratios are tested. The experimental results indicate that the lateral deflection dominates the buckling failure of the specimens. The ultimate bearing capacity of the specimens is enhanced gradually as the RSCC strength grade increases but decreases as the slenderness ratio rises. The load-strain curves are linear and basically coincide at the elastic stage. The decrease in the slenderness ratio or increase in the RSCC strength grade contributes to the improvement of the stiffness and ultimate circumferential and axial strains of the columns gradually. Based on the combined tangent modulus theory and bearing capacity of the RSCCFCST short columns, two estimation models are presented to predict the ultimate bearing capacity of the RSCCFCST long columns. Additionally, comparisons between the calculation results of the ultimate strength demonstrate that the prediction models established in this study are more accurate than the other specifications mentioned.

An artificial neural network to estimate soil temperature

1997 ◽  
Vol 77 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Chun-Chieh Yang ◽  
Shiv O. Prasher ◽  
Guy R. Mehuys
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Potential Evapotranspiration ◽  
Daily Rainfall ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Soil Temperatures ◽  
Ann Models ◽  
Artificial Neural

This study was undertaken to develop an artificial neural network (ANN) model for transient simulation of soil temperature at different depths in the profile. The capability of ANN models to simulate the variation of temperature in soils was investigated by considering readily available meteorologic parameters. The ANN model was constructed by using five years of meteorologic data, measured at a weather station at the Central Experimental Farm in Ottawa, Ontario, Canada. The model inputs consisted of daily rainfall, potential evapotranspiration, and the day of the year. The model outputs were daily soil temperatures at the depths of 100, 500 and 1500 mm. The estimated values were found to be close to the measured values, as shown by a root-mean-square error ranging from 0.59 to 1.82 °C, a standard deviation of errors from 0.61 to 1.81 °C, and a coefficient of determination from 0.937 to 0.987. Therefore, it is concluded that ANN models can be used to estimate soil temperature by considering routinely measured meteorologic parameters. In addition, the ANN model executes faster than a comparable conceptual simulation model by several orders of magnitude. Key words: Artificial neural networks, soil temperature, precipitation, potential evapotranspiration

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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