Development Of An Artificial Neural Network Ann Model For Estimating Cemented Paste Backfill Performance
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Development Of An Artificial Neural Network Ann Model For Estimating Cemented Paste Backfill Performance
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Author : Reza Sadat Alhosseini
language : en
Publisher:
Release Date : 2009
Development Of An Artificial Neural Network Ann Model For Estimating Cemented Paste Backfill Performance written by Reza Sadat Alhosseini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Applications Of Artificial Intelligence In Mining And Geotechnical Engineering
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Author : Hoang Nguyen
language : en
Publisher: Elsevier
Release Date : 2023-11-20
Applications Of Artificial Intelligence In Mining And Geotechnical Engineering written by Hoang Nguyen and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-20 with Science categories.
Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering provides recent advances in mining, geotechnical and geoengineering, as well as applications of artificial intelligence in these areas. It serves as the first book on applications of artificial intelligence in mining, geotechnical and geoengineering, providing an opportunity for researchers, scholars, engineers, practitioners and data scientists from all over the world to understand current developments and applications. Topics covered include slopes, open-pit mines, quarries, shafts, tunnels, caverns, underground mines, metro systems, dams and hydro-electric stations, geothermal energy, petroleum engineering, and radioactive waste disposal. In the geotechnical and geoengineering aspects, topics of specific interest include, but are not limited to, foundation, dam, tunneling, geohazard, geoenvironmental and petroleum engineering, rock mechanics, geotechnical engineering, soil mechanics and foundation engineering, civil engineering, hydraulic engineering, petroleum engineering, engineering geology, etc. - Guides readers through the process of gathering, processing, and analyzing datasets specifically tailored for mining, geotechnical, and engineering challenges. - Examines the evolution and practical implementation of artificial intelligence models in predicting, forecasting, and optimizing solutions for mining, geotechnical, and engineering problems. - Offers cutting-edge methodologies to address the most demanding and complex issues encountered in the fields of mining, geotechnical studies, and engineering.
Modeling The Effects Of Sulphate And Curing Temperature On The Strength Of Cemented Paste Backfill Using Artificial Neural Networks
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Author : Libardo Enrique Orejarena
language : en
Publisher:
Release Date : 2010
Modeling The Effects Of Sulphate And Curing Temperature On The Strength Of Cemented Paste Backfill Using Artificial Neural Networks written by Libardo Enrique Orejarena and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with University of Ottawa theses categories.
Estimating The Compressive Strength Of Portland Cement Using Artificial Neural Network
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Author : Henok Hunduma
language : en
Publisher:
Release Date : 2013
Estimating The Compressive Strength Of Portland Cement Using Artificial Neural Network written by Henok Hunduma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
The purpose of this thesis is to develop Artificial Intelligence Models to predict the 28-days compressive strength of Portland cement (CCS). Two models, Artificial Neural Network and Fuzzy Logic were created using 4 input parameters of Portland cement that comprise both the physical and chemical characteristics. C3S, C2S, Alkali, and Cement fineness, were used as input variables to predict one outcome of compressive strength. Early strength prediction in the production process instead of waiting 28 days for the test to be completed could significantly improve the quality of the cement and reduce the cost associated with the waiting period. Data collected from literature was applied to predict the compressive strength of Portland cement. A rectangular mold of cement and water was created and kept in a temperature of 20° with 90% relative humidity for 24 hours. The cured sample was then stored in a water bath for 27 days and 6 identical bars were tested. The original data had twenty input parameters of cement with one output of compressive strength. The four most significant input parameters were selected for this particular revision. Out of the 150 generated points 100 were used to train the models while 50 data points were applied in the testing of the system. The average percentage errors achieved were 4.2% and 5.8 % for the fuzzy logic model and ANN model respectively. The results indicated that Artificial Intelligence (AI) could be a useful tool for the prediction of cement strength, and through the application of fuzzy logic algorithms, a more user friendly and more explicit model than the ANN could be produced within successful low error margins.
Development Of Artificial Neural Network Software And Models For Engineering Materials
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Author : Abdallah F. Bseiso
language : en
Publisher:
Release Date : 2021
Development Of Artificial Neural Network Software And Models For Engineering Materials written by Abdallah F. Bseiso and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Artificial Neural Network (ANN), which is inspired by biological neural networks in the human brain, is one important tool of machine learning that creates artificial intelligence through computational systems. The creation of this intelligence is contingent on learning from available data regarding a specific subject. Although machine learning, in general, has profuse applications in most scientific disciplines, yet few have been developed in civil engineering due to the required time consuming and demanding programming. In order to minimize this, intelligible ANN software has been developed in this research capable of training networks with any number of hidden layers and nodes for each layer. Furthermore, two models have been created to demonstrate the robust applications of ANN. The first application involves a simulation of the strain-temperature behavior of a shape memory alloy (SMA) under thermal cycling. In the second case, the bond strength between the concrete and the steel-reinforced bars is predicted considering the effects of steel corrosion level, concrete compressive strength, and concrete cover. Java programming language was used in developing the ANN software and a simple graphical user interface (GUI) has been designed, allowing the user to control the inputs and the training progress, make predictions and save the outputs. In this study, the ANN models were developed with different structures and activation functions to prove the ANN eminent idiosyncrasy of modeling data from different fields. Comparison is made between these models as well as models created by statistical regression and other models available in the literature. The developed software can efficiently train ANNs with any structure, as less time is needed to develop one ANN using the software than using programming methods. Moreover, the user will have the option to save the weights and the biases at any iteration and predict responses for the currently trained or previously trained ANN. The model predicted results can be saved or exported as an excel file. In terms of the created models, ANN can capture highly complicated relationships accurately and effectively compared to traditional modeling methods. Based on that, more accurate predictions are expected using ANN.
Development And Applications Of Artificial Neural Network For Prediction Of Ultimate Bearing Capacity Of Soil And Compressive Strength Of Concrete
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Author : Seyed Jamalaldin Seyed Hakim
language : en
Publisher:
Release Date : 2006
Development And Applications Of Artificial Neural Network For Prediction Of Ultimate Bearing Capacity Of Soil And Compressive Strength Of Concrete written by Seyed Jamalaldin Seyed Hakim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Concrete categories.
Artificial Neutral Networks (ANNs) have recently been widely used to model some of the human activities in many areas of science and engineering. One of the distinct characteristics of the ANNs is its ability to learn from experience and examples and then to adapt with changing situation. ANNs does not need a specific equation from the differs from traditional prediction models. Instead of that, its need enough input-output data. Also, it can continously re-train the new data, so that it can conveniently adapt to new data. The research work focuses on development and application of artificial neural networks in some specific civil engineering problems such as prediction of ultimate bearing capacity of soil and compressive strength of concrete after 28 days. One of the main objectives of this study was the development and application of an ANN for predicting of the ultimate bearing capacity of soil. Hnece, a large training set of actual ultimate bearing capacity of soil cases was used to train the network. A neural network model was developed using 1600 data set of nine inputs including the width foundation, friction angle in three layer, cohession of three layers and depth of first and second layer are selected as input of predicting of ultimate bearing capacity in soil. The model contained a training data set of 1180 cases, a verification data set of 240 cases and a testing data set of 240 cases. The training was terminated when the average training error reached 0.002. Many combinations of layers, number of neurons, activation function, different values for learning rate and momentum were considered and the results were validated using an independent validation data set. Finally 9-15-1 is chosen as the architecture of neural network in study. That means 9 inputs with a set of 15 neurons in hidden layer has the most reaonable agreement architecture. This architecture gave high accuracy and reasonable Mean Square Error (MSE). The network computes the mean squared erroe between the actual and predicted values for output over all patterns. Calculation of mean percentage relative error for training set data, show that artifial neural network predicted ultimate bearing capacity with error of 14.83%. The results prove that the artificial neural network can work sufficiency for predicting of ultimate bearing capacity as an expert system. It was observed that overall construction-related parameters played a role in affecting ultimate bearing capacity, buts especially the parameter "friction angle" play most important role. An important observation is that influencing of the parameters "cohesion" is too less than another parameters for calculating of ultimate bearing capacity of soil.
Applications Of Computational Intelligence In Concrete Technology
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Author : Sakshi Gupta
language : en
Publisher: CRC Press
Release Date : 2022-06-23
Applications Of Computational Intelligence In Concrete Technology written by Sakshi Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Technology & Engineering categories.
Computational intelligence (CI) in concrete technology has not yet been fully explored worldwide because of some limitations in data sets. This book discusses the selection and separation of data sets, performance evaluation parameters for different types of concrete and related materials, and sensitivity analysis related to various CI techniques. Fundamental concepts and essential analysis for CI techniques such as artificial neural network, fuzzy system, support vector machine, and how they work together for resolving real-life problems, are explained. Features: It is the first book on this fast-growing research field. It discusses the use of various computation intelligence techniques in concrete technology applications. It explains the effectiveness of the methods used and the wide range of available techniques. It integrates a wide range of disciplines from civil engineering, construction technology, and concrete technology to computation intelligence, soft computing, data science, computer science, and so on. It brings together the experiences of contributors from around the world who are doing research in this field and explores the different aspects of their research. The technical content included is beneficial for researchers as well as practicing engineers in the concrete and construction industry.
Estimation Of Quantiles In A Simulation Model Based On Artificial Neural Networks
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Author : Sevda Alaca
language : en
Publisher: GRIN Verlag
Release Date : 2017-07-10
Estimation Of Quantiles In A Simulation Model Based On Artificial Neural Networks written by Sevda Alaca and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-10 with Mathematics categories.
Master's Thesis from the year 2017 in the subject Mathematics - Stochastics, grade: 1,3, Technical University of Darmstadt, language: English, abstract: This thesis deals with the development of an "alpha"-quantile estimate based on a surrogate model with the use of artificial neural networks. Using artificial neural networks as an estimate is considered a nonparametric approach. The estimation of a specific quantile of a data population is a widely used statistical task and a comprehensive way to discover the true relationship among variables. It can be classified as nonparametric regression, where it is one of the standard tasks. The most common selected levels for estimation are the first, second and third quartile (25, 50 and 75 percent). The quantile level is given by "alpha". A 25 percent quantile for example has 25 percent of the data distribution below the named quantile and 75 percent of the data distribution above it. Sometimes the tail regions of a population characteristic are of interest rather than the core of the distribution. Quantile estimation is applied in many different contexts - financial economics, survival analysis and environmental modelling are only a few of them.
Software Estimation Artificial Neural Networks
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Author : Zahid Hussain Wani
language : en
Publisher:
Release Date : 2023-05-23
Software Estimation Artificial Neural Networks written by Zahid Hussain Wani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-23 with categories.
Software Estimation Artificial Neural Networks (SEANN) is a cutting-edge approach to estimating software development effort using artificial neural networks (ANNs). It leverages the power of machine learning and neural networks to predict the time, resources, and effort required to complete software development projects. SEANN is designed to address the challenges and complexities associated with software estimation, which traditionally relies on subjective judgment and expert opinion. By utilizing ANNs, SEANN can learn from historical data and identify patterns, relationships, and dependencies that impact software development effort. This enables more accurate and reliable estimations compared to traditional methods. The core of SEANN lies in its neural network architecture, which is inspired by the structure and functioning of the human brain. The network consists of interconnected nodes, or artificial neurons, organized into multiple layers. Each neuron receives input signals, performs computations, and produces an output signal that is passed on to the next layer. Through a process of training, the network adjusts the connections between neurons to optimize its performance and improve estimation accuracy.
Simulation Based Construction Productivity Improvement Using Neural Network Driven Fuzzy Reasoning System
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Author : Seyedfarid Mirahadi
language : en
Publisher:
Release Date : 2013
Simulation Based Construction Productivity Improvement Using Neural Network Driven Fuzzy Reasoning System written by Seyedfarid Mirahadi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.