Development And Applications Of Artificial Neural Network For Prediction Of Ultimate Bearing Capacity Of Soil And Compressive Strength Of Concrete
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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.
Proceedings Of Awam International Conference On Civil Engineering 2022 Volume 2
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Author : Nuridah Sabtu
language : en
Publisher: Springer Nature
Release Date : 2024-01-03
Proceedings Of Awam International Conference On Civil Engineering 2022 Volume 2 written by Nuridah Sabtu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Technology & Engineering categories.
This book gathers the latest research, innovations, and applications in the field of civil engineering, as presented by leading national and international academics, researchers, engineers, and postgraduate students at the AWAM International Conference on Civil Engineering 2022 (AICCE’22), held in Penang, Malaysia on February 15-17, 2022. The book covers highly diverse topics in the main fields of civil engineering, including structural and earthquake engineering, environmental engineering, geotechnical engineering, highway and transportation engineering, water resources engineering, and geomatic and construction management. In line with the conference theme, “Sustainability And Resiliency: Re-Engineering the Future”, which relates to the United Nations’ 17 Global Goals for Sustainable Development, it highlights important elements in the planning and development stages to establish design standards beneficial to the environment and its surroundings. The contributions introduce numerousexciting ideas that spur novel research directions and foster multidisciplinary collaborations between various specialists in the field of civil engineering. This book is part of a 3-volume series of these conference proceedings, it represents Volume 2 in the series.
Optimization Of Tuned Mass Dampers
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Author : Gebrail Bekdaş
language : en
Publisher: Springer Nature
Release Date : 2022-04-07
Optimization Of Tuned Mass Dampers written by Gebrail Bekdaş and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-07 with Technology & Engineering categories.
This book is a timely book to summarize the latest developments in the optimization of tuned mass dampers covering all classical approaches and new trends including metaheuristic algorithms. Also, artificial intelligence and machine learning methods are included to predict optimum results by skipping long optimization processes. Another difference and advantage of the book are to provide chapters about several types of control types including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers and inerter dampers. Tuned mass dampers (TMDs) are vibration absorber devices used in all types of mechanic systems. The key factor in the design is an effective tuning of TMDs for the desired performance. In practice, several high-rise structures and bridges were designed by including TMDs. Also, TMDs were installed after the construction of the structures after several negative experiences resulting from the disturbing sway of the structures. In optimum design, several closed-form expressions have been proposed for optimum frequency and damping ratio of TMDs, but the exact optimization requires iterative optimization approaches. The current trend is to use evolutionary algorithms and metaheuristic optimization methods to reach the goal.
Transactions Of The American Society Of Civil Engineers
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Author : American Society of Civil Engineers
language : en
Publisher:
Release Date : 2003
Transactions Of The American Society Of Civil Engineers written by American Society of Civil Engineers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Civil engineering categories.
Vols. 29-30 contain papers of the International Engineering Congress, Chicago, 1893; v. 54, pts. A-F, papers of the International Engineering Congress, St. Louis, 1904.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2005
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Dissertations, Academic categories.
Applied Mechanics Reviews
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Author :
language : en
Publisher:
Release Date : 1992
Applied Mechanics Reviews written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Mechanics, Applied categories.
Conference Papers Index
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Author :
language : en
Publisher:
Release Date : 1987
Conference Papers Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Engineering categories.
Artificial Neural Network To Predict The Compressive Strength Of Semilightweight Concrete Containing Ultrafine Ggbs
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Author : P. Parthiban
language : en
Publisher:
Release Date : 2020
Artificial Neural Network To Predict The Compressive Strength Of Semilightweight Concrete Containing Ultrafine Ggbs written by P. Parthiban and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Lightweight concrete categories.
Design strength is usually determined after a 28-day curing period as per codal provisions. The prediction of compressive strength before curing reduces waiting time and expedites regular construction activity. The aim of this study is to develop a neural network model to predict the 28-day compressive strength of semilightweight concrete (sLWC) containing ultrafine ground granulated blast-furnace slag (UFGGBS). In this investigation, a novel lightweight coarse aggregate that is made up of wood ash was used to prepare sLWC. Six input parameters, such as cement, UFGGBS as cement replacement, lightweight wood ash pellets as coarse aggregate, fine aggregate, water content, and superplasticizer, were used to train the model. The 28-day compressive strength was taken as an output parameter. A total of 384 data was collected from 24 sLWC mixes, each containing 16 specimens, and trained in an artificial neural network (ANN) using a feedforward-backpropagation model. Trained data were validated with a set of tested data. The correlation coefficient R 2 values for trained and tested data were 0.932 and 0.917, respectively, with least errors. The study concluded that ANN was a reliable and fast tool for predicting the compressive strength of sLWC. It also efficiently reduced cost and time.
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.
Prediction And Evaluation Of Hardened Concrete Strength
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Author : Yidong XU
language : en
Publisher: Springer
Release Date : 2025-07-31
Prediction And Evaluation Of Hardened Concrete Strength written by Yidong XU and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-31 with Technology & Engineering categories.
This open access book monitors the development of the temperature field within concrete structures and, based on the Arrhenius equation, constructs F-P maturity equations applicable to different temperature ranges. It investigates the impact of hydration rate on the strength prediction method of the maturity equation. Furthermore, the book employs artificial neural network theory to improve the accuracy of early concrete strength predictions, optimizing the neural network model to develop a more precise and widely applicable prediction model. An intelligent program is developed using MATLAB, facilitating rapid strength prediction and assessment on construction sites using measured parameters.