International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
We looked at the data. Here’s a breakdown of some key statistics about the nation’s incoming presidents’ addresses, how long they spoke, how well, and more.
32 Ways a Digital Marketing Consultant Can Help Grow Your BusinessBarry Feldman
How can a digital marketing consultant help your business? In this resource we'll count the ways. 24 additional marketing resources are bundled for free.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/emoji
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
This paper demonstrates a framework that entails a bottom-up approach to
accelerate research, development, and verification of neuro-inspired sensing
devices for real-life applications. Previous work in neuromorphic
engineering mostly considered application-specific designs which is a strong
limitation for researchers to develop novel applications and emulate the true
behaviour of neuro-inspired systems. Hence to enable the fully parallel
brain-like computations, this paper proposes a methodology where a spiking
neuron model was emulated in software and electronic circuits were then
implemented and characterized. The proposed approach offers a unique
perspective whereby experimental measurements taken from a fabricated
device allowing empirical models to be developed. This technique acts as a
bridge between the theoretical and practical aspects of neuro-inspired
devices. It is shown through software simulations and empirical modelling
that the proposed technique is capable of replicating neural dynamics and
post-synaptic potentials. Retrospectively, the proposed framework offers a
first step towards open-source neuro-inspired hardware for a range of
applications such as healthcare, applied machine learning and the internet of
things (IoT).
Associative memory implementation with artificial neural networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
Microscopy images segmentation algorithm based on shearlet neural networkjournalBEEI
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks.
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks. Predicting consumer behavior, creating and
understanding more sophisticated buyer segments, marketing automation, content creation and
sales forecasting are some applications of the ANN systems in the marketing. In this paper, a review
in recent development and applications of the Artificial Neural Networks is presented in order to move
forward the research filed by reviewing and analyzing recent achievements in the published papers.
Thus, the developed ANN systems can be presented and new methodologies and applications of the
ANN systems can be introducedArtificial Neural Networks (ANNs), or more simply neural networks, are new systems and computational
methods for machine learning, knowledge demonstration, and finally the application of knowledge
gained to maximize the output responses of complex systems (Chen et al. 2019). An Artificial Neural
Network (ANN) is a data processing model based on the way biological nervous systems, such as the
brain, process data. They're focused on the neuronal structure of the mamalian cerebral cortex, but at
a much smaller scale. Many artificial intelligence experts believe that artificial neural networks are the Artificial neural networks are designed in the same way as the human brain, with neuron nodes
interconnected in a web-like fashion. Neurons are billions of cells that make up the human brain. Each
neuron is made up of a cell body that processes information by bringing it to and from the brain (inputs
and outputs) (Van Gerven and Bohte 2017). The main idea of such networks is (to some extent) inspired
by the way the biological neural system works, to process data, and information in order to learn and
create knowledge. The key element of this idea is to create new structures for the information
processing system. The Artificial neural network architecture is shown in the figure 2 (Bre, Gimenez,
and Fachinotti 2018).The system is made up of a large number of highly interconnected processing elements called neurons
that work together to solve a problem and transmit information through synapses (electromagnetic
connections). The neurons are interconnected closely and organized into layer. The input layer receives the data, while the output layer generates the final result. Between the two, one or more secret layers are typically sandwiched. This arrangement makes predicting
ANALYSIS ON MACHINE CELL RECOGNITION AND DETACHING FROM NEURAL SYSTEMSIAEME Publication
One of several major components of a production system is the arrangement, which may considerably affect the cost of internal material handling as well as the flexibility, efficiency, and supervision of the plant. To cut the cost of warehouse management and setup time, cellular manufacturing is a technique that organizes the equipment needed to produce similar products into unit cells. In conjunction with traditional nonlinear relapse or chunk analysis techniques, neural networks are widely used for quantifiable analysis and information modeling. They are typically applied in this way to problems that may be stated in terms of categorizing or measurement. These recommendations update three different ANN algorithms genome Wide. The BP Networking, the KSOM Network, and thus the ART1 Connections are standard techniques. We use such non - linear and non-CF ANN methods for the adjustment of MPIM cell reproduction and proportionate cellular development for both the measurement with considering manufacturing things into consideration.
A recurrent neural network with a self-organizing structure based on the dynamic analysis of a task is presented in this paper. The stability of the recurrent neural network is guaranteed by design. A dynamic analysis method to sequence the subsystems of the recurrent neural network according to the fitness between the subsystems and the target system is developed. The network is trained with the network's structure self-organized by dynamically activating subsystems of the network according to tasks. The experiments showed the proposed network is capable of activating appropriate subsystems to approximate different nonlinear dynamic systems regardless of the inputs. When the network was applied to the problem of simultaneously soft measuring the chemical oxygen demand (COD) and NH3-N in wastewater treatment process, it showed its ability of avoiding the coupling influence of the two parameters and thus achieved a more desirable outcome.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
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1. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
333 | P a g e
Prediction of Properties of Self Compacting Concrete Using
Artificial Neural Network
Abdul Raheman*, Prof. P. O. Modani**
*(M E Structural Engineering, Department of Civil Engineering, B N College of Engineering, Pusad
** (Department of Civil Engineering, B N College of Engineering, Pusad
ABSTRACT
This study deals with artificial neural
network (ANN) modeling to predict properties of
the SCC. Data developed experimentally for
SCC. The data used in the models are arranged
in the format of seven input parameters that
cover the contents of cement, sand, coarse
aggregate, fly ash sand, water and water/powder
ratio, superplasticizer dosage and an output
parameter that is compressive strengths at 7
days, 28 days, 90 days, slump flow, T50 cm, V-
funnel and L-Box. respectively for ANN. The
importance of different input parameters is also
given for predicting the strengths at various ages
using neural network. It was observed that the
ANN model can predict the properties of the
SCC quite well with correlation coefficients, with
very low root mean square errors and also check
for different Epoch with the use of Neuro
Solution Software. This study shows that, as an
alternative to classical modeling techniques, the
ANN approach can be used to accurately predict
the performance parameters of SCC.
Keywords - artificial neural network, compressive
strength, learning rate, self-compacting concrete.
I. INTRODUCTION
Self-Compacting Concrete (SCC), which
flows under its own weight and does not require any
external vibration for compaction, has
revolutionized concrete placement. Self compacting
concrete (SCC) is a mixture of new concrete
technologies used in developed countries, such as
Japan, Europe and the United States of America
(Ouchi Masahiro, 2003). SCC first developed and
used in Japan since 1989 (Okumura Hajime and
Ouchi Masahiro, 2003), in order to obtain concrete
structure which has high durability and easy to pour
the concrete mix into every corner of the mold,
eliminating the noise pollution generated by the
vibrator, produce smooth concrete surface without
any additional finishing work, and need less
manpower. The hardened concrete of SCC is dense,
homogeneous and has the same properties and
durability as conventional concrete.
The mix design principles of SCC, as
compared to conventional concrete, contains: lower
coarse aggregate content, increased paste content,
low water- Powder ratio, increased superplasticiser,
and sometimes a viscosity modifying admixture.
There is no standard method for SCC mix
design and many academic institutions, ready-mixed
industries; precast and contracting companies have
developed their own mix proportioning methods.
Mix designs often use volume as a key parameter
because of the importance of the need to over fill the
voids between the aggregate particles. Some
methods try to fit available constituents to an
optimized grading envelope. Another method is to
evaluate and optimize the flow and stability of first
the paste and then the mortar fractions before the
coarse aggregate is added and the whole SCC mix
tested. So in doing trial and error technique requires
a long time and needs more concrete material to
overcome the problems, need a tool for evaluating
concrete mix composition of SCC.
In recent years, ANNs have been applied to
many civil engineering applications with some
degree of success. ANNs have been applied to
geotechnical problem like prediction of settlement
of shallow foundations. Researchers have also used
ANN in structural engineering. Some researchers
have recently proposed a new method of mix design
and prediction of concrete strength using neural
network. Also, several works were reported on the
use of neural network based modeling approach in
predicting the concrete strength. Some attempts
have been made to describe the compressive
strength properties using traditional regression
analysis tools and statistical models . ANN is
applied to many structural engineering problem such
as [1] modeling flexural behavior of Fiber
Reinforced Concrete beams [2], predicting large
deflection response [3], flexible pavement thickness
modeling [4] and many more. Artificial neural
network is considered as one of the most important
applications of artificial intelligence, which found
considerable importance in practical problems. They
attempt to simulate the human thinking in solving a
particular problem.
However, the development of neural
network models for predicting the strength of SCC
has not been fully investigated. Artificial neural
network (ANN) has been proposed as an alternative
method for solving certain difficult problems in
selecting the best mix proportion for required
characteristics where the conventional techniques
2. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
334 | P a g e
have not achieved the desired speed, accuracy and
efficiency.
Therefore, the objective of the present
study was to examine the potential of ANN for
predicting the 28-day compressive strength and
workability of SCC mixtures, with the data obtained
experimentally. The complex relationship between
mixture proportions and engineering properties of
SCC was generated based on data obtained
experimentally. It was observed that the neural
network could effectively predict compressive
strength in spite of intricate data and could be used
as a tool to support decision making, by improving
the efficiency of the process
II. ARTIFICIAL NEURAL NETWORK
A neural network, also known as a parallel
distributed processing network, as computing
paradigm that is loosely modeled after structures of
the brain. It consists of interconnected processing
elements called nodes or neurons that work together
to produce an output function. The output of a
neural network relies on the cooperation of the
individual neurons within the network to operate.
Processing of information by neural networks is
characteristically done in parallel rather than in
series (or sequentially) as in earlier binary
computers on Von Neumann machines. Since it
relies on its member neurons collectively to perform
its function, a unique property of a neural network is
that it can still perform its overall function even if
some of the neurons are not functioning. In other
words it is robust to tolerate error or failure. (See
fault tolerant) Additionally, neural networks are
more readily adaptable to fuzzy logic computing
tasks than are Von Neumann machines.
Neural network theory is sometimes used
to refer to a branch of computational science that
uses neural networks models to simulate or analyze
complex phenomena and/or study the principles of
operation of neural networks analytically. It
addresses problems similar to artificial intelligence
(AI) except that AI uses traditional computational
algorithms to solve problems whereas neural
networks use „networks of agents‟ (software or
hardware entities linked together) as the
computational architecture to solve problems.
Neural networks are trainable systems that can
“learn” to solve complex problems from a set of
exemplars and generalize the “acquired knowledge”
to solve unforeseen problems as in stock market and
environmental prediction .i.e., they are self-adaptive
systems.
Traditionally, the term neural network has
been used to refer to a network of biological
neurons. In modern usage, the term is often used to
artificial neural networks, which are composed of
artificial neurons or nodes. The term „Neural
Network‟ has two distinct points:
Biological neural networks are made up of
real biological neurons that are connected or
functionally-related in the peripheral nervous system
or the central nervous system. In the field of
neuroscience, they are often identified as groups of
neurons that perform a specific physiological
function in laboratory analysis.
Artificial neural networks are made up of
interconnecting artificial neurons (usually simplified
neurons) designed to model (or mimic) some
properties of biological neural networks. Artificial
neural networks can be used to model the modes of
operation of biological neural networks, whereas
cognitive models are theoretical models that mimic
cognitive brain functions without necessarily using
neural networks while artificial intelligence are
well-crafted algorithm that solve specific intelligent
problems (such as chess playing, pattern
recognition, etc.) without using neural network as
the computational architecture.
2.1 MODEL OF AN ARTIFICIAL NEURON:
The human brain no doubt is a highly
complex structure viewed as a massive, highly
interconnected network of simple processing
elements called Neurons. Every component of the
model bears a direct analogy to the actual
components of a biological neuron and hence is
termed as Artificial Neuron. It is this model which
forms the basis of the Artificial Networks.
From fig. 1.2, x1, x2, x3, …. c are the n
inputs to the artificial neuron. w1, w2, w3 … wn are
the weights attached to the input links.
A biological neuron receives all inputs
through the dendrites, sums them and produces an
output if the sum is greater than the threshold value.
The input signals are passed on to the cell body
through the synapse, which may accelerate or retard
an arriving signal. It is this acceleration or
retardation of the input signals modeled by the
weights. An effective synapse, which transmits a
stronger signal, will have a correspondingly larger
weight while a weak synapse will have smaller
weights. Thus, weights here are Multicative factors
of the inputs to account for the strength of the
synapse. Hence, the total input I received by the
soma of the artificial neuron is
I= w1 x1 + w2 x2 + …… + wn xn
= wi xi
To generate the final output y, the sum is
passed on to the non-linear filter Φ called as
Activation function or Transfer function or Squash
function, which releases the output.
Y= Φ(I)
A very commonly used Activation
function is the Threshold function. In this sum is
compared with a threshold value θ. If the value of I
is greater than θ, then the output is 1 else it is 0.
3. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
335 | P a g e
Y= Φ ( ∑wi xi –θ), ……. (
i=1 to n)
Where, Φ is the step function known as Heaviside
function and is such that
Φ(I)=1, I>0
=0, I<0
2.2. Sigmoidal Function:
This function is a continuous function that
varies gradually between the asymptotic values 0
and 1 or -1 and +1 and is given by
Ø (I) = 1/ (1+e-ὰt
)
Where is the slope parameter, which adjusts to
the abruptness of the functions as it changes
between two asymptotic values. Sigmoidal functions
are differential, which is an important feature of
Neural Network Theory.
Fig 2.1 Simple model of an artificial network
III. ANN METHODOLOGY
Artificial Neural Network models are
specified by topology, node characteristics and
training or learning rules. These rules specify an
initial set of weights and indicate how weights
should be adopted during improvement of
convergence performance [4]. Broadly there are two
types of ANN models namely supervised and
unsupervised. In case of supervised both input
patterns and output patterns are known during
training [5]. The present paper adopts feed forward
supervised ANN model for prediction of „Concrete
mix proportion‟. The possible training parameters
are number of iterations (epoch) learning rate, error
goal and number of hidden layers. These parameters
are varied until a good convergence of ANN
training is obtained and there by fixing the optimal
training parameters. These optimal parameters are
used for testing and validation process. The general
computational ANN model is always represented by
a term topology which represents number of neurons
in input layer, hidden layer and output layer as
depicted in the figure [2.1]. However the numbers of
neurons in the input layer and output layer are
determined based on the problem domain depending
upon number of input variables and number of
output or target variables. The number of hidden
layers and neurons in hidden layer are fixed during
the training process. The specific ANN topology
model adopted in this research work is depicted in
the fig [3.1].
Fig 3.1 Topology to predict properties of SCC
The model‟s success in predicting the
behavior of SCC mixtures depends on
comprehensiveness of the training data. Availability
of large variety of experimental data was required to
develop the relationship between the mixture
variables of SCC and its measured properties. The
basic parameters considered in this study were
cement content, sand content, coarse aggregate
content, fly ash content, water-to-powder ratio and
superplasticizer dosage. A database of 31 mixes
from the experimentally was retrieved having
mixture composition with comparable physical and
chemical properties. The ANNs were designed using
31 pairs of input and output vectors for strength
prediction, the predicted results obtained from
neural network were compared with the
experimental values obtained experimentally. The
training of ANNs was carried out using pair of input
vector and output vector. The complete list of input
and output data is given in Table 7.1 and Table 3.2.
5. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
337 | P a g e
Output Data
Eval
uating the design mixes of SCC using ANNs is the
aim of this study; an ANN model is constructed,
trained, tested, and validated in the Neuro solution
software using the available test data of concrete
mix-designs. The ANN model evaluated the various
properties of SCC. Architecture of ANN uses seven
neurons in the input layer, one hidden layer with
several neurons, and seven unit output layers. While
the input layer consists of six neurons that are the
amo
unt
of cement (kg/m3), coarse aggregate (kg/m3), Fine
aggregate (kg/m3), fly ash (kg/m3), Superplastisizer
(lit/m3), water cement ratio (%) and Water Powder
ratio (%). The hidden layer uses sigmoid activation,
and the output layer consists of seven neuron that
are compressive strength of SCC at 7,28 and 90
days, slump flow, T50 cm, V-funnel and L-Box.
The data were randomly divided into a
training phase, testing phase, and validation phase.
Back propagation neural network is a three-layered
Mix Slump
flow
(mm)
T50cm
(sec)
V-
funnel
T5min
c (sec)
L-box
Blocking
ratio(H2/H1)d
7- days
(MPa)
28-
days
(MPa)
90-
days
(MPa)
1 720 1.65 5.08 0.97 19.40 27.78 35.33
2 620 2.61 6.22 0.90 15.20 22.58 27.25
3 670 2.37 6.01 0.92 28.16 38.07 46.07
4 620 2.15 6.86 0.90 23.50 32.00 39.22
5 680.00 2.10 5.10 0.78 22.23 33.54 48.40
6 740.00 1.95 4.55 0.79 30.22 43.55 57.30
7 745.00 4.00 5.20 0.77 30.10 43.80 63.80
8 750.00 2.03 5.40 0.85 26.20 38.45 61.30
9 695.00 4.00 5.43 0.82 29.35 39.53 50.45
10 740.00 2.54 5.60 0.83 34.91 42.00 53.10
11 750.00 2.20 6.70 0.89 32.65 39.35 61.10
12 705.00 4.85 5.50 0.84 33.35 40.45 55.90
13 772.50 2.00 4.90 0.81 38.46 40.00 55.30
14 725.00 2.47 4.95 0.79 26.25 35.11 57.30
15 720.00 1.98 4.70 0.80 30.40 38.20 65.60
16 747.50 2.38 5.68 0.84 30.47 39.12 53.60
17 717.50 2.10 5.80 0.83 24.22 41.34 52.90
18 715.00 1.97 6.90 0.84 26.70 34.46 47.30
19 705.00 1.90 6.02 0.87 31.35 31.35 59.26
20 725.00 2.77 6.22 0.86 28.65 33.55 51.53
21 705.00 1.94 5.98 0.88 28.22 32.60 56.60
22 700.00 1.90 6.78 0.78 25.34 36.90 58.45
23
24
25
26
27
28
29
30
31
6. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
338 | P a g e
feed forward architecture. The three layers are input
layer, hidden layer and output layer
IV. RESULTS AND DISCUSSION
The acceptance / rejection of the model
developed are determined by its ability to predict the
properties of SCC. Also, a successfully trained
model is characterized by its ability to predict
properties value for the data it was trained on.8 fold
cross validation is used to predict the properties of
SCC for the data set used in this study. The cross
validation is the method of accuracy of a
classification or regression model. The input data set
is divided into several parts (a number defined by
the user), with each part intern used to test a model
fitted to the remaining part. The correlation
coefficient, root mean square error (RMSE), and
mean absolute error is used to judge the
performance of the neural network approach in
predicting the strength. Neural networks can be
effective for analyzing a system containing a
number of variables, to establish patterns and
characteristics not previously known
In the present study, Mix design of SCC
using ANN using Neuro Solution Software has been
carried out. The objective of this study is to predict
various properties of SCC
After Training the given data, the train
neural network diagram is obtained as shown in Fig.
4.1. A graph also obtains which gives the error of
each step of the training and summarizes the number
of epoch in each state (The graph below shows the
MSE (mean square error) Vs Epoch). This data is
shown in Graph.4.1
Fig. 4.1 The Train Neural Network Diagram
Fig. 4.2 The Graph Mean square error Vs Epoch
After testing data a graph obtains, this
graph gives the relation between the input values
(i.e. output parameters) that we gave and predicted
output which are given by the Neuro solution
Fig. 4.3 The Graph Output vs. Exemplar
Table 4.1
Predicted output which is given by the Neuro
solution
M
ix
Slu
mp
flow
(m
m)
T5
0c
m
(se
c)
V-
fu
nn
el
T5
mi
n c
(se
c)
L-box
Blocki
ng
ratio(
H2/H1
)d
7-
day
s
(M
Pa)
28-
da
ys
(M
Pa
)
90-
da
ys
(M
Pa
)
2
3
609.
035
1.5
6
4.2
9
0.89
12.5
9
20.
36
58.
78
2
4
657.
932
5
4.5
5
4.3
1
0.84
19.6
8
38.
97
66.
62
2
5
744.
481
9
4.4
3
4.4
5
0.90
30.4
2
45.
87
70.
20
2
6
785.
445
1
3.8
0
7.1
8
0.89
38.8
8
46.
06
70.
20
2
7
662.
496
1
1.9
6
4.2
7
0.87
14.8
7
26.
94
66.
91
2
8
754.
057
4.9
9
6.7
4
0.83
37.0
7
46.
29
70.
15
2
9
732.
114
2
4.8
5
4.2
6
0.94
18.0
2
44.
84
70.
36
3
0
741.
592
1
5.2
4
4.2
8
0.89
26.2
8
46.
24
70.
22
3
1
770.
616
8
5.2
0
5.7
6
0.8413
71
36.8
509
5
46.
31
69.
12
7. Abdul Raheman / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp. 333-339
339 | P a g e
V. CONCLUSION
In this study, the Artificial Neural Network
is used for prediction of Properties of SCC. The
back propagation neural network has been
developed for prediction of SCC. Effect of various
parameters like W/C ratio, w/p ratio, admixtures,
has been studied. Present study shows that back
propagation neural network could be trained for
future prediction of SCC properties. The predicted
properties of SCC from neural network are very
close to the actual properties of SCC measured
experimentally.
Following observations were made
regarding neural network-
1.Cement, FA, CA, fly ash, water, w/p ratio, sp were
given as input and compressive strength
of SCC at 7,28 and 90 days, slump flow, T50 cm, V-
funnel and L-Box was estimated using different
structures of ANN. These can be used to train the
neural network for getting the estimated values of
SCC.
2. Predicted values and experimental output values
of properties of SCC for different combination of
Concrete mix ingredient depicted. The Correlation
coefficient is 0.999 between estimated values of
properties of SCC and predicted values of same
from ANN. The present study signifies the strong
accuracy of predicted values. All the results
obtained in this study supports usage of ANN.
3. And also to be able to train a neural network,
there must be either a logical linear relation or a
logical non linear relation between the input and
output.
This study of Artificial Neural Network
model will provide an efficient and rapid means of
obtaining optimal solutions to predict the optimum
mix proportions for specified strength and
workability for sustainable SCC. It is possible to
produce SCC mix design with various compositions
for a given range of targeted slumps and
compressive strength and others properties. The
application of ANN in the field of SCC mix design
is very appropriate in order to preserve and
disseminate valuable experience and innovation
efficiently at reasonable cost.
REFERENCES
Journal Papers:
[1] Paratibha Aggarwal, Rafat Siddiq, Yogesh
Aggarwal, Surinder M Gupta “Self-
Compacting Concrete - Procedure for Mix
Design” Leonardo Electronic Journal of
Practices and Technologies, January-June
2008
[2] Paratibha Aggarwal, Yogesh Aggarwal,
“Prediction of Compressive Strength of
SCC Containing Bottom Ash using
Artificial Neural Networks” World
Academy of Science, Engineering and
Technology 53 2011
[3] N.Krishna Raju and Y. Krishna reddy, “ A
critical review of the Indian, British and
American methods of concrete mix design”
, The Indian Concrete Journal, April 1989.
[4] Akhmad Suryadi, Triwulan, Pujo Aji
“Artificial Neural Networks for Evaluating
the Compressive Strength of Self
Compacting Concrete” Journal of Basic
and Applied Scientific Research 2010
[5] M. Jamil, M.F.M. Zain, H.B.Basri, “Neural
Network Simulator Model foR
Optimization in High Performance
Concrete Mix Design” European Journal
of Scientific Research
[6] Keerthi Gowda B.S, G. L. Easwara Prasad
“Forecasting of SFRSCC‟s Fresh Property
by ANN” International Journal of Earth
Sciences and Engineering, October 2011.
Books:
1. M S Shetty, Concrete Technology (S Chand
and Copany Ltd. New Delhi, 2011).
2. S.Rajasekaran, “Neural Networks, Fuzzy
Logic, and Genetic Algorithms”, (PHI
Learning Ltd. New Delhi 2008 pp. 12-84.)
.
Thesis:
Rishi Garg, Concrete Mix Design Using
Artificial Neural Networks, Thapar Institute of
Engineering and Technology (Deemed
University) Patiala – 147004.
Proceedings Papers:
Marianne Tange Jepsen, “Predicting concrete
durability by using artificial neural network”
featured at the proceedings “Durability of
exposed concrete containing secondary
cementitious materials, Hirtshals, November
2002.