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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 50 • WINTER 2010

Intelligent Infrastructure: Neu-ral Networks, Wavelets and Chaos Theory for Intelligent

Transportation Systems and Smart Structures” constitutes a practical service-oriented guide to large scale infrastructures. It pro-vides a unique treatise for attack-ing and solving some of the most complex and intractable problems encountered in the emerging fields of transportation systems and technologies based on image and signal processing and com-putational intelligence. Chapters 2, 3, and 4 are introductory chap-ters, which aim to summarize the computer science theories that are incorporated in this book for intelligent transportation systems and intelligent buildings. Specifi-cally, chapter 2 provides an intro-duction and a brief presentation of several kinds of Artificial Neu-ral Networks (ANNs). The basic principles of wavelets, as an im-age/signal processing method based on multi-resolution analy-sis, are then discussed in chapter 3. The computer science theory part of the book ends with chap-ter 4, which summarizes the key ideas of chaos theory such as state space, time delay, and embedding dimension.

Chapters 5 through 17 demon-strate solid works on transporta-tion and structural engineering, split in 2 application groups. The first part (chapters 5 to 12) deal with topics concerning intelligent freeways and extend the work pre-sented in [1] incorporating wave-lets, ANNs and Fuzzy Logic (FL) for several applications that are most of the times- mutually im-plicative. It is worthwhile to men-tion that Adeli and Samant [2, 3] pioneered the concept of wavelets in transportation engineering for the first time in 2000. The second part involves methodologies for smart structures and infrastruc-tures and aims (i) at providing an early warning of structural fail-ures to reduce the loss of lives and property, (ii) at improving the un-

derstanding of structural behavior under external loads, and (iii) at facilitating proactive, rational de-cisions with respect to infrastruc-ture sustainability.

Neural networks trained on the basis of Levenberg-Marquardt (LM) Backpropagation (BP) are imple-mented in chapters 5 and 6. The BP learning is based on the gradient de-scent along the error surface (more details can be found in [4]). On the other hand, LM-BP is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued functions [5, 6]. In this book LM-BP is the classifi-cation schema of incidents occurring in freeways based on speed, volume, and occupancy measurements after a combination of wavelet de-noising

Review of the Book “Intelligent Infrastructure”

Digital Object Identifier 10.1109/MITS.2010.939928

“

Christos-Nikolaos E. Anagnostopoulos was born in Athens, Greece in 1975. He received his Mechanical Engineering Diploma from the National Technical University of Athens (NTUA) in 1998, and the Ph.D. degree from the Electrical and Computer Engineering Dpt., NTUA in 2002.

From 2008, he serves the University of the Aegean as Assistant Professor in the Cultural Technology and Communication Department. He is a member of the Greek chamber of Engineers and member of IEEE. His research interests include image processing, computer vision, neural

networks and artificial intelligence. He has published more than 100 papers in journals and conferences, in the above subjects as well as other related fields in informatics. He also serves as associate editor for the IEEE Intelligent Transportation Systems Magazine.

Reviewer: Christos-Nikolaos Anagnostopoulos

Date of publication: 4 February 2011

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 51 • WINTER 2010

and statistical clustering analysis (chapter 5). Similarly, in chapter 6, a Levenberg-Marquardt neural network classifies traffic flow into free flow, transitional flow, or congested flow with the use of a micro- simulation model for tracking travel times of vehicles.

In the next chapters, Adeli and Jiang examine the problem of free-way work zones estimation in terms of capacity, queue delay and cost assessment. The proposed meth-ods are based on macroscopic Boltzmann-simulated annealing [7] neural network model (chapter 7), adaptive neuro-fuzzy logic model for freeway work zone capacity esti-mation (chapter 8) and subtractive clustering techniques in Radial Ba-sis Function (RBF) [6] and BP neu-ral networks for assisting work zone engineers to create effective traffic management plans (chapter 9). The entire model is described in chapter 10 and is integrated in an interactive software (IntelliZone). The first part of applications ends with chapters 11 and 12, describing wavelet analysis of traffic flows and dynamic time-delay wavelet neural network (WNN) mod-el for traffic flow forecasting. The model incorporates both time and day information of the prediction time. This way, it can be used for long-term (LT) and short-term (ST) traffic flow forecasting. The former (LT) could be of great interest in planning appli-cations, while the latter (ST) could be applied to on-line Intelligent trans-portation Systems applications.

In chapters 13–17, the authors examine multidisciplinary method-ologies of smart assemblies where intelligent sensors and actuators are cleverly integrated for infrastructure monitoring and control under various scenarios of external dynamic loads due to winds or earthquakes. In chap-ters 13, 14 and 15 Adeli and Jiang as-sess Fuzzy Wavelet Neural Networks (FWNN) [8] in three distinctive ap-plications. The model is based on the integration of four different comput-ing concepts, namely dynamic neural networks, wavelets, fuzzy logic and chaos theory. The results of these chapters can be used for real-time health monitoring, damage detection, and control of large-scale structures (e.g. high-rising building structures). The application of the WNN model is also extended in active nonlinear con-trol of large three- dimensional (3D) building structures. The latter is de-scribed in chapter 16 along with two numerical examples of twelve-storey and eight-storey irregular (i.e., com-plex shaped) buildings. The results demonstrate that a dynamic fuzzy wavelet neuroemulator model based on the model described in chapter 13, can accurately predict the responses of irregular buildings. The book con-cludes with a chapter dedicated to a Genetic Algorithm (GA) approach for the estimation of the appropriate con-trol forces in every control loop in a building subjected to earthquake re-cords. GAs became extremely popu-lar through the work of John Holland

in the early 1970s [9] and during the last twenty years have had numerous applications in engineering.

As our daily life heavily depends on large scale transportation infrastruc-ture, which includes both telematics and construction networks, it is essen-tial to ensure both their smooth opera-tion and their effective management. This book addresses solutions for im-proved automation in traffic networks and health-monitoring control in high-rise and complex structures. There is no question that this is an ambitious book, and it is somehow hard to be-lieve that it is authored by two authors only. Adeli and Jiang cover a wide range of engineering problems from a systems point of view, offering a guide-book that brings together outstanding approaches for making infrastructures more intelligent, more secure and therefore more sustainable. “Intelli-gent Infrastructure” could serve as a valuable handbook for engineers, es-pecially those who work on interdis-ciplinary topics and the integration of technologies that eventually converge.

References[1] H. Adeli and A. Kassem, Wavelets in Intelli-

gent Transportation Systems. Hoboken, NJ: Wiley, 2005.

[2] A. Samant and H. Adeli, “Feature extraction for traffic incident detection using wavelet transform and linear discriminant analy-sis,” Computer-Aided Civil Infrastruc. Eng., vol. 15, no. 4, pp. 241–250, July 2000.

[3] H. Adeli and A. Samant, “An adaptive con-jugate gradient neural network–wavelet model for traffic incident detection,” Com-puter-Aided Civil Infrastruc. Eng., vol. 15, no. 4, pp. 251–260, July 2000.

[4] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Pearson, Prentice-Hall, 1998.

[5] K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Q. Appl. Math., vol. 2, no. 2, pp. 164–168, July 1944.

[6] D. W. Marquardt, “An algorithm for the least-squares estimation of nonlinear pa-rameters,” SIAM J. Appl. Math., vol. 11, no. 2, pp. 431–441, June 1963.

[7] E. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines: A Stochastic Ap-proach to Combinatorial Optimization and Neural Computing. New York: Wiley, 1989.

[8] D. W. C. Ho, P. A. Zhang, and J. Xu, “Fuzzy wavelet networks for function learning,” IEEE Trans. Fuzzy Syst., vol. 9, no. 1, pp. 200–211, Feb. 2001.

[9] J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press, 1975.

Title: Intelligent Infrastructure:Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures

Authors: Hojjat Adeli, Xiaomo Jiang

ISBN/ Press/Year: 9781420085365/ CRC Press/2008

Price: $167.95

WWW link: http://www.crcpress.com/product/isbn/9781420085365;jsessionid=FC7QNX9OSB259-t3Tn1PYw**