CONTRIBUTION OF AI TO THE CONSTRUCTION WORLD

2021 ◽  
Vol 12 (1) ◽  
pp. 58-70
Author(s):  
Kanhaiya Kumar ◽  
◽  
Muskan Kumari
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Simulated Annealing ◽  
Chaos Theory ◽  
Fuzzy Systems ◽  
Firefly Algorithm ◽  
Civil Engineering ◽  
Complicated Structure ◽  
Know How ◽  
Intelligent Computer

Artificial intelligence is a department of computer science and information technological know-how, involved in the research, layout, and application of intelligent computer. Conventional techniques for modeling and optimizing complicated structure systems require big amounts of computing assets, and artificial-intelligence-primarily based solutions can frequently provide treasured alternatives for successfully solving problems inside the civil engineering. This paper summarizes currently evolved methods and theories within the growing path for programs of synthetic intelligence in civil engineering, such as evolutionary computation, neural networks, fuzzy systems, professional machine, reasoning, type, and learning, in addition to others like chaos theory, cuckoo seek, firefly algorithm, know-how-based engineering, and simulated annealing. The primary studies tendencies are also talked about in the end. The paper presents an overview of the advances of synthetic intelligence carried out in civil engineering.

scholarly journals Artificial Intelligence in Civil Engineering

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Pengzhen Lu ◽  
Shengyong Chen ◽  
Yujun Zheng
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Systems ◽  
Firefly Algorithm ◽  
Civil Engineering ◽  
Complex Structure ◽  
Cuckoo Search ◽  
Research Trends ◽  
Main Research ◽  
Knowledge Based ◽  
Intelligent Computer

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

scholarly journals Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

2021 ◽  
Author(s):  
Rabia Saleem ◽  
Bo Yuan ◽  
Fatih Kurugollu ◽  
Ashiq Anjum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Process Model ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Black Box ◽  
Decision Making Process ◽  
Deterministic Models ◽  
Know How

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decision- making is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2006 ◽  
pp. 166-187 ◽  
Author(s):  
Juan R. Rabunal ◽  
Juan Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

Neuro-Fuzzy Models and Applications

2019 ◽  
pp. 78-98
Author(s):  
Sushruta Mishra ◽  
Soumya Sahoo ◽  
Brojo Kishore Mishra
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Production Control ◽  
Accurate Solution ◽  
Execution Phase ◽  
Neuro Fuzzy ◽  
Neural Network Theory

The modern techniques of artificial intelligence have found application in almost all the fields of human knowledge. Among them, two important techniques of artificial intelligence, fuzzy systems (FS) and artificial neural networks (ANNs), have found many applications in various fields such as production, control systems, diagnostic, supervision, etc. They evolved and improved throughout the years to adapt arising needs and technological advancements. However, a great emphasis is given in the engineering field. The techniques of artificial intelligence based on fuzzy logic and neural networks are frequently applied together for solving engineering problems where the classic techniques do not supply an easy and accurate solution. Separately, each one of these techniques possesses advantages and disadvantages that, when mixed together, provide better results than the ones achieved with the use of each isolated technique. As ANNs and fuzzy systems have often been applied together, the concept of a fusion between them started to take shape. Neuro-fuzzy systems were born which utilize the advantages of both techniques. Such systems show two distinct ways of behavior. In a first phase, called learning phase, it behaves like neural networks that learn internal parameters off-line. Later, in the execution phase, it behaves like a fuzzy logic system. A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. Neural networks introduce its computational characteristics of learning in the fuzzy systems and receive from them the interpretation and clarity of systems representation. Thus, the disadvantages of the fuzzy systems are compensated by the capacities of the neural networks. These techniques are complementary, which justifies its use together. This chapter deals with an analysis of neuro-fuzzy systems. Benefits of these systems are studied with its limitations too. Comparative analyses of various categories of neuro-fuzzy systems are discussed in detail. Apart from these, real-time applications of such systems are also presented.

scholarly journals Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

2021 ◽  
Author(s):  
Rabia Saleem ◽  
Bo Yuan ◽  
Fatih Kurugollu ◽  
Ashiq Anjum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Process Model ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Black Box ◽  
Decision Making Process ◽  
Deterministic Models ◽  
Know How

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decision- making is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2011 ◽  
pp. 1084-1101
Author(s):  
Juan R. Rabunal ◽  
Jerónimo Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

Applications of Computational Intelligence in Computing Security

2020 ◽  
pp. 1-22
Author(s):  
Yousif Abdullatif Albastaki
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems

This chapter is an introductory chapter that attempts to highlight the concept of computational intelligence and its application in the field of computing security; it starts with a brief description of the underlying principles of artificial intelligence and discusses the role of computational intelligence in overcoming conventional artificial intelligence limitations. The chapter then briefly introduces various tools or components of computational intelligence such as neural networks, evolutionary computing, swarm intelligence, artificial immune systems, and fuzzy systems. The application of each component in the field of computing security is highlighted.

Artificial Neural Networks Ensemble Used for Pipeline Leak Detection Systems

2008 ◽  
Author(s):  
Inhau´ma N. Ferraz ◽  
Ana C. B. Garcia ◽  
Fla´via C. Bernardini
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Fuzzy Systems ◽  
Leak Detection ◽  
Detection Systems ◽  
Neural Networks Ensemble ◽  
Uncertainty Environment ◽  
Low Uncertainty

The physical and operational properties of pipelines vary greatly. There is thus no universally applicable method, external or internal, which possesses all the features and the functionality required for a perfect leak detection performance. The authors of this paper know quite well that traditional methods, in a low uncertainty environment, overcome artificial intelligence methods of leak detection systems. If one considers the real world as a creator of uncertainties, neural networks and fuzzy systems emerge as important promising technologies for the development of leak detection systems. In this work, we propose a method for constructing ensembles of ANNs for pipeline leak detection. The results obtained in our experiments were satisfactory.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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