Journal ArticleDOI
Prediction of Pavement Performance through Neuro‐Fuzzy Reasoning
TLDR
The proposed neuro‐fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.Abstract:
: Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro-fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF-THEN fuzzy rules. The results of the neuro-fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro-fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.read more
Citations
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Journal ArticleDOI
System identification in structural engineering
Gene F. Sirca,Hojjat Adeli +1 more
TL;DR: A review of representative research reported in journal articles in the field of structural system identification published in journals since 1995 is presented in this article, which is divided into five sections based on the general approach used: conventional model-based, biologically-inspired, signal processing-based and multi-paradigm approaches.
Journal ArticleDOI
Artificial Intelligence in Civil Engineering
TL;DR: 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, are summarized.
Journal ArticleDOI
Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review
TL;DR: A widespread review on various platform and image processing approaches for asphalt surface interpretation based on crack interpretation related to asphalt pavements and a survey of the developed pavement inspection systems up to date are presented.
Journal ArticleDOI
Smart structures: Part II — Hybrid control systems and control strategies
Nicholas Fisco,Hojjat Adeli +1 more
TL;DR: In this paper, the authors reviewed significant work done on active and semi-active vibration control of structures performed in the past decade or so, and reviewed improved or new control strategies developed for civil structures.
Journal ArticleDOI
Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems
Mehran Ahmadlou,Hojjat Adeli +1 more
TL;DR: In this paper a new method, called Visibility Graph Similarity (VGS), is presented as a method of measuring Generalized Synchronization, and it is shown that VGS provides a more accurate measure of the overall synchronization compared with SL.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Journal ArticleDOI
On the approximate realization of continuous mappings by neural networks
TL;DR: It is proved that any continuous mapping can be approximately realized by Rumelhart-Hinton-Williams' multilayer neural networks with at least one hidden layer whose output functions are sigmoid functions.
Journal ArticleDOI
Networks for approximation and learning
Tomaso Poggio,Federico Girosi +1 more
TL;DR: Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks as mentioned in this paper, and two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering.
Book
Foundations of neuro-fuzzy systems
TL;DR: The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.
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