The objectives of this project were (1) to improve the defect detection reliability and (2) to improve the inspection speed of conventional rail defect detection methods. The prototype developed in this work uses noncontact transducers, ultrasonic guided waves, and real-time statistical pattern recognition to improve system reliability. The prototype was field tested seven times with the support of the Federal Railroad Administration (FRA) R-4 hy-railer vehicle. The latest tests at Herzog, Inc., in June 2010 showed excellent results. Two blind tests resulted in a defect detection reliability exceeding industry average and American Railway Engineering and Maintenance-of-Way Association recommendations. These tests also proved the potential for distinguishing good welds from defective welds, an excellent detestability of the vertical split head defect, and the potential for characterizing different rail surface conditions for grinding management purposes. Within this project, a 250-foot-long rail defect farm was also constructed at the University of California--San Diego, with in-kind donations from BNSF Railway, for the development of this and other rail inspection technologies of interest to FRA and to the railroad industry.

The Federal Railroad Administration (FRA) Office of Research and Development's Track and Structures Program sponsored a study for developing and testing a rail defect detection system based on ultrasonic guided waves and non-contact probing. Current rail defect detection systems based on ultrasonic testing have limitations in terms of reliability of defect detection, inspection speed, and other drawbacks associated with the requirement for contact between the ultrasonic probes and the rail surface. More importantly, conventional ultrasonic testing of rails has serious difficulties detecting internal defects in the presence of surface shelling. The rail defect detection technique that is being funded is based on fundamentally new concepts in that 1) uses ultrasonic waves traveling along, rather than across the rail running direction, 2) uses non-contact means of generating and detecting the ultrasonic waves in the rail, and 3) uses advanced signal processing algorithms to de-noise the measurements and extract robust defect-sensitive information. A prototype is being assembled based on this technology and plans are in place to install and test the prototype in the FRA Research Car.

The Federal Railroad Administration (FRA) Office of Research and Development's Track and Structures Program sponsored a study for developing and testing a rail defect detection system based on ultrasonic guided waves and non-contact probing. Current rail defect detection systems based on ultrasonic testing have limitations in terms of reliability of defect detection, inspection speed, and other drawbacks associated with the requirement for contact between the ultrasonic probes and the rail surface. More importantly, conventional ultrasonic testing of rails has serious difficulties detecting internal defects in the presence of surface shelling. The rail defect detection technique that is being funded is based on fundamentally new concepts in that 1) uses ultrasonic waves traveling along, rather than across the rail running direction, 2) uses non-contact means of generating and detecting the ultrasonic waves in the rail, and 3) uses advanced signal processing algorithms to de-noise the measurements and extract robust defect-sensitive information. A prototype is being assembled based on this technology and plans are in place to install and test the prototype in the FRA Research Car.

Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. This Special Issue covers a wide range of related topics such as innovative sensors and sensing technologies for crack, displacement, and sudden event monitoring, sensor optimization, and novel sensor data processing algorithms for damage and defect detection, operational modal analysis, and system identification of a wide variety of structures (bridges, transmission line towers, high-speed trains, masonry light houses, etc.).

The rapid expansion of transportation industries worldwide, including railways, and the never-ending desire to reduce travel time have highlighted the need to resort to advanced transit systems. Conventional railway systems have been modified to make them travel at much higher speeds. High-Speed Rail includes the main topics and basic principles of high-speed railways (HSRs). The book reflects new engineering and track developments, the most current design methods, as well as the latest industry standards and policies. It provides a comprehensive overview of the significant characteristics for HSRs; highlights recent advancements, requirements, and improvements; and details the latest techniques in the global market. High-Speed Rail contains a collection of the latest research developments on HSRs. This book comprehensively covers basic theory and practice in sufficient depth to provide a solid grounding for railway engineers. It also helps readers maximize effectiveness in all facets of HSRs. This professional book as a credible source and a valuable reference can be very applicable and useful for professors, researchers, engineers, practicing professionals, trainee practitioners, students, and others interested in HSRs.

This book highlights the applications of data mining technologies in structural dynamic analysis, including structural design, optimization, parameter identification, model updating, damage identification, in civil, mechanical, and aerospace engineering. These engineering applications require precise structural design, fabrication, inspection, and further monitoring to obtain a full life-cycle analysis, and by focusing on data processing, data mining technologies offer another aspect in structural dynamic analysis. Discussing techniques in time/frequency domain, such as Hilbert transforms, wavelet theory, and machine learning for structural dynamic analysis to help in structural monitoring and diagnosis, the book is an essential reference resource for beginners, graduates and industrial professionals in various fields.