Chapters authored
Human Factors and Ergonomics Model and Application in Automotive Industries: Focus on Job Satisfaction
Part of the book: New Trends and Developments in Automotive Industry
Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison
Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain.
Part of the book: Recent Trends in Artificial Neural Networks
Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques
More than a billion structures exist on our planet comprising a million bridges. A number of these infrastructures are near to or have already exceeded their design life and maintaining their health condition is an engineering optimization problem. Besides, these assets are damage-prone during their service life. This is due to the fact that different external loads induced by the environmental effects, overloading, blast loads, wind excitations, floods, earthquakes, and other natural disasters can disturb the serviceability and integrity of these structures. To overcome such bottlenecks, structural health monitoring (SHM) systems have been used to guarantee the safe functioning of structures to make satisfactory decisions on structural maintenance, repair, and rehabilitation. However, conventional SHM approaches such as virtual inspections cannot be used for structural continuous monitoring, real-time and online assessment. Therefore, soft computing techniques can be significantly used to mitigate the aforesaid concerns by handling the qualitative analysis of the complex real world behavior. This chapter aims to introduce the optimized SHM-based soft computing techniques of bridge structures through artificial intelligence and machine learning algorithms in order to illustrate the performance of advanced bridge monitoring approaches, which are required to maintain the health condition of infrastructures as well as to protect human lives.
Part of the book: Applied Methods in Design and Construction of Bridges, Highways and Roads