Intelligent computing for predicting axial capacity of drilled shafts (original) (raw)
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Artificial Intelligent Models to Predict Drilled Shaft Capacity
2015
Designing large civil engineering structures can be very problematic given unstable soil conditions because foundations may not be able to support the structure. In the case of unstable soil layers, drilled shafts (deep foundations) are installed. They are responsible for carrying the whole structure load to a stable soil layer. Hence the prediction of the capacity of the drilled shafts will play a key role in the design of drilled shaft foundations. To understand the behavior of the shaft, load tests are performed on the field, however, it is challenging to analyze their failure loads because often strain-displacement curves obtained from field tests are not well defined. With this in mind, the purpose of this paper is to use fuzzy logic to model the behavior of drilled shaft foundations in tension, compression, and shear given their geometry and the soil properties of the adjacent ground. The model for this study has been developed through MATLAB. Depending on the test type (tension, compression, or shear), inputs have been selected for the fuzzy model. They are different in each test. The elastic limit and the ultimate capacity of soil are the output of this model. The simulation results show that the proposed modeling framework is very effective in predicting the complex behavior of drilled pile shafts in compression, tension, and shear.
2017
The presence of cemented soils pose significant challenges in drilled shaft design and may prevent accurate estimates of the service limit state if traditional analytical techniques are employed. Thus, an Artificial Neural Network (ANN) is developed and tested as an alternative method for predicting settlement induced by axial loads. Training is carried out using the results of 31 field load tests performed in Las Vegas, USA, where cemented soils are common, and an automated process is employed to determine the optimal network architecture. Ultimately, a cascaded feed-forward ANN with one hidden layer consisting of six artificial neurons produced the highest quality generalization. Ten additional load tests not included in the original training, testing, or validation datasets are reserved to evaluate performance. It is observed that the ANN produces similarly accurate estimates of load-settlement on average as compared to two more traditional t-z style approaches.
Analysis Ultimate Bearing Capacity on Bored Pile with Using Artificial Neural Network
The issues that often arise within geotechnical engineering include uncertainty, complexity, and inaccuracies in planning. Therefore, this creates problems as relying on assumptions are the only way to determine parameters in design and construction. Recently, a new approach has emerged, inspired by the intelligence of the human brain, and it is called artificial neural network (ANN). This study aimed to utilize the ANN models with a back-propagation algorithm that feeds forward to predict the ultimate bearing capacity, namely NN_Q ult . The total number of samples used are 375, and the input variables are d, Lp Le, A, K, f'c, N tip , N shaft , and P. According to Shahin (2001), the model was divided into two group: 2/3 training data and 1/3 validation data, processed in a modified ANN program. The prediction results of NN_Qult are then compared with the carrying capacity of pile driving analysis (PDA). It shows a good relationship, as evidenced by the value of R2> 0.8 and RMSE close to 0.1. The sensitivity analysis (AS) was also carried out to obtain the level of influence of the input compared to the output which are 12, 367%; 10.255%; 14.576%; 8.323%; 15.870%; 5.154%; 8.218%; 14.314%; 10.923% respectively. The L e , N tip and P variables are the most influenced of the dataset.
Computational intelligence based prediction of drilling rate of penetration: A comparative study
Journal of Petroleum Science and Engineering, 2019
Application of artificial intelligence in the accurate prediction of the rate of penetration (ROP), an important measure of drilling performance, has lately gained significant interest in oil and gas well drilling operations. Consequently, several computational intelligence techniques (CITs) for the prediction of ROP have been explored in the literature. This study explores the predictive capabilities of four commonly used CITs in the prediction of ROP and experimentally compare their predictive performance. The CIT algorithm utilizes predictors which are easily accessible continuous drilling data that have physical but complex relationship with ROP based on hydromechanical specific energy ROP model. The four CITs compared are the artificial neural network (ANN), extreme learning machine, support vector Regression and least-square support vector regression (LS-SVR). Two experiments were carried out; the first experiment investigates the comparative performance of the CITs while the second investigates the effect of reduced number of predictors on the performance of the models. The results show that all the CITs perform within acceptable accuracy with testing root mean square error range (RMSE) of 18.27-28.84 and testing correlation coefficient (CC) range of 0.71-0.94. LS-SVR has the best predictive performance in terms of accuracy with RMSE of 18.27 and CC of 0.94 while ANN has the best testing execution time at 0.03 secs. Also utilizing the specific energy concept in chosen drilling parameters to be included among the predictors shows improved performance with five drilling parameters showing an improvement of 3%-9% in RMSE for LS-SVR in the two well studied. The utilization of the specific energy concept in the selection of the predictors in this study has demonstrated that the easily accessible drilling parameters have immense value to provide acceptable performance in the development of ROP model with CITs.
2020
Artificial Neural Networks (ANN) have been applied to many interesting problems in different areas of science, medicine and engineering and in some cases, they provide stateof-the-art solutions. This paper investigates the application of an ANN model in mining to predict the Drillability Index (DI) of a rock mass given rock parameters such as uniaxial compressive strength, shear strength, tensile strength, abrasion and hardness. Drillability indicates whether penetration is easy or hard while penetration rate indicates whether it is fast or slow. Therefore, prediction of the drillability and penetration rate is very important in rock drilling. Penetration rate is a necessary value for the cost estimation and the planning of the drilling project. According to results of this study, Uniaxial Compressive Strength (UCS) rating has the highest weight of 0.051083 among the three parameters studied which reconfirms the literature review finding which indicates that UCS is the most importan...
An intelligent approach to evaluate drilling performance
Neural Computing and Applications
In this paper, an attempt has been made to predict the rate of penetration (ROP) of rocks by incorporating thrust, revolutions per minute (rpm), flushing media and compressive strength of rocks using artificial neural network (ANN) technique. A three-layer feedforward back-propagation neural network with 4-7-1 architecture was trained using 472 experimental data sets of sandstone, limestone, rock phosphate, dolomite, marble and quartz-chlorite-schist rocks. A total of 146 new data sets were used for the testing and comparison of the ROP by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of ROP. The coefficient of determination by ANN was 0.985, while coefficient of determination was 0.629 for rate of penetration. The mean absolute error (MAE) for rate of penetration by ANN was 0.3547, whereas MAE by MVRA was 1.7499.
Different Neural Networks and Modal Tree Method for Predicting Ultimate Bearing Capacity of Piles
2018
The prediction of the ultimate bearing capacity of the pile under axial load is one of the important issues for many researches in the field of geotechnical engineering. In recent years, the use of computational intelligence techniques such as different methods of artificial neural network has been developed in terms of physical and numerical modeling aspects. In this study, a database of 100 prefabricated steel and concrete piles is available from existing publications to solve issues related to pile’s bearing capacity analysis. Three different artificial neural network algorithms were developed for comparing and verifying the obtained results at analyzing the bearing capacity of pile in soils. During the modeling process, the geometric properties of different piles, soil materials properties, friction angle and flap numbers (hammer blows) were selected as input parameters to the selected network and the output from the network was considered as the bearing capacity of the pile. Fi...
A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling
The International Journal of Advanced Manufacturing Technology, 2007
This paper describes the comparison of the burr size predictive models based on artificial neural networks (ANN) and response surface methodology (RSM). The models were developed based on three-level full factorial design of experiments conducted on AISI 316L stainless steel work material with cutting speed, feed, and point angle as the process parameters. The ANN predictive models of burr height and burr thickness were developed using a multilayer feed forward neural network, trained using an error back propagation learning algorithm (EBPA), which is based on the generalized delta rule. The performance of the developed ANN models were compared with the second-order RSM mathematical models of burr height and thickness. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. The details of experimentation, model development, testing, and performance comparison are presented in the paper.
Computers and Geotechnics, 2009
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on the results of cone penetration test (CPT) data. Approximately, 300 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.
Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of un-cemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on large-and low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that...