Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

This study was devoted to examine pile bearing capacity and to provide a reliable model to simulate pile load-settlement behaviour using a new artificial neural network (ANN) method. To achieve the planned aim, experimental pile load test were carried out on model open-ended steel piles, with pile aspect ratios of 12, 17, and 25. An optimised second-order Levenberg-Marquardt (LM) training algorithm has been used in this process. The piles were driven in three sand densities; dense, medium, and loose. A statistical analysis test was conducted to explore the relative importance and the statistical contribution (Beta and Sig) values of the independent variables on the model output. Pile effective length, pile flexural rigidity, applied load, sand-pile friction angle and pile aspect ratio have been identified to be the most effective parameters on model output. To demonstrate the effectiveness of the proposed algorithm, a graphical comparison was performed between the implemented algorithm and the most conventional pile capacity design approaches. The proficiency metric indicators demonstrated an outstanding agreement between the measured and predicted pile-load settlement, thus yielding a correlation coefficient (R) and root mean square error (RMSE) of 0.99, 0.043 respectively, with a relatively insignificant mean square error level (MSE) of 0.0019. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group

TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop a novel hybrid intelligent system, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the imperialism competitive algorithm (ICA), ANFIS-PNN-ICA for prediction of TBM performance. In fact, the role of ICA in this hybrid system is to optimize the membership functions obtained by ANFIS-PNN model for receiving a higher level of performance prediction. Based on previously published works, seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength, rock mass weathering, the uniaxial compressive strength, revolution per minute and thrust force were set as inputs to predict TBM performance. Together with the ANFIS-PNN-ICA model, two single model of PNN and ANFIS were also constructed for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of several performance indices, e.g., correlation coefficient (R). R values of (0.9642, 0.9654 and 1), (0.9482, 0.9671 and 0.9778) and (0.9652, 0.9642, 0.9898) were obtained for training, testing and all datasets of PNN, ANFIS and ANFIS-PNN-ICA models, respectively. These results revealed that the greater prediction capacity can be provided by the ANFIS-PNN-ICA predictive model compared to ANFIS and PNN models and this hybrid intelligent model can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction

A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material

Since determining the rock deformation directly in the laboratory is costly and time consuming, it is important to reliably determine/estimate this parameter through the use of several simple rock index tests. This study develops a new hybrid intelligent technique according to Takagi–Sugeno Fuzzy Inference System-Group Method of Data Handling optimized by the particle swarm optimization, called TS Fuzzy-GMDH-PSO for prediction of the rock deformation. The PSO role in this advanced system is to optimize the membership functions of TS Fuzzy-GMDH model for enhancing the level of prediction capacity. In this research, four rock index tests including Schmidt hammer, p-wave velocity, porosity and point load were selected and conducted in laboratory in order to establish a suitable database for prediction purposes. To demonstrate the feasibility and applicability of the advanced hybrid model, two base models of TS Fuzzy and GMDH were also modeled to forecast rock deformation. After conducting several sensitivity analyses on the mentioned models to get the highest performance capacity, their prediction levels were evaluated using some statistical indices, such as root mean square error and correlation coefficient (R). The comparative results confirmed the superiority of the TS Fuzzy-GMDH-PSO over other two models, namely TS Fuzzy and GMDH in terms of both train and test phases. It can be concluded that the TS Fuzzy-GMDH-PSO can be recommended as a powerful, capable and new model to solve the problems related to rock strength and deformation

Experimental Study and Kinetic Modeling of Decoking of Pacol Process Dehydrogenation Catalyst

The Pt/γ-Al2O3 catalyst life time was limited by the formation of coke on the external and internal surfaces of catalyst in dehydrogenation reactors. The kinetics of decoking of dehydrogenation catalyst was studied in a pilot scale fixed bed reactor experimentally. The effects of temperature, oxygen concentration and other operating conditions on decoking process were investigated. A kinetic model was deve-loped to describe the decoking of mentioned catalyst. An objective function was defined as the sum of squares of the deviations among the calculated and plant data. Accordingly the appropriate values were found in order to minimize this function. It was concluded that there was a good agreement between simulation results and experimental data.  © 2015 BCREC UNDIP. All rights reservedReceived: 18th September 2014; Revised: 28th February 2015; Accepted: 9th March 2015How to Cite: Toghyani, M., Rahimi, A., Mamanpoush, M., Kazemian, R., Harandizadeh, A.H. (2015). Experimental Study and Kinetic Modeling of Decoking of Pacol Process Dehydrogenation Catalyst. Bulletin of Chemical Reaction Engineering &amp; Catalysis, 10 (2): 155-161. (doi:10.9767/bcrec.10.2.7357.155-161) Permalink/DOI: http://dx.doi.org/10.9767/bcrec.10.2.7357.155-161  </p

Experimental Study and Kinetic Modeling of Decoking of Pacol Process Dehydrogenation Catalyst

The Pt/γ-Al2O3 catalyst life time was limited by the formation of coke on the external and internal surfaces of catalyst in dehydrogenation reactors. The kinetics of decoking of dehydrogenation catalyst was studied in a pilot scale fixed bed reactor experimentally. The effects of temperature, oxygen concentration and other operating conditions on decoking process were investigated. A kinetic model was deve-loped to describe the decoking of mentioned catalyst. An objective function was defined as the sum of squares of the deviations among the calculated and plant data. Accordingly the appropriate values were found in order to minimize this function. It was concluded that there was a good agreement between simulation results and experimental data.  © 2015 BCREC UNDIP. All rights reservedReceived: 18th September 2014; Revised: 28th February 2015; Accepted: 9th March 2015How to Cite: Toghyani, M., Rahimi, A., Mamanpoush, M., Kazemian, R., Harandizadeh, A.H. (2015). Experimental Study and Kinetic Modeling of Decoking of Pacol Process Dehydrogenation Catalyst. Bulletin of Chemical Reaction Engineering &amp; Catalysis, 10 (2): 155-161. (doi:10.9767/bcrec.10.2.7357.155-161) Permalink/DOI: http://dx.doi.org/10.9767/bcrec.10.2.7357.155-161  </p

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated