Tomonori Nagayama

Industrialized nations have a huge investment in the pervasive civil infrastructure on
\nwhich our lives rely. To properly manage this infrastructure, its condition or serviceability should be reliably assessed. For condition or serviceability assessment, Structural Health Monitoring (SHM) has been considered to provide information on the current state of structures by measuring structural vibration responses and other physical phenomena and conditions. Civil infrastructure is typically larg

Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities. However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems. Limited network resources (e.g. battery power, storage space, bandwidth, etc.) make these issues quite challenging. This paper first investigates the e

Ambient vibration measurement is an important tool to evaluate the integrity of in-service structures. The writers apply a new structural identification method to ambient vibration data taken from a full-scale suspension bridge. The method consists of two steps: identification of vibration modes and inverse analysis of structural properties from the identified modes. For modal identification, the method treats the structure as a multi-input–multioutput system, distinguishing noise from true mode

Wireless smart sensor networks (WSSNs) have been proposed by a number of researchers to evaluate the current condition of civil infrastructure, offering improved understanding of dynamic response through dense instrumentation. As focus moves from laboratory testing to full-scale implementation, the need for multi-hop communication to address issues associated with the large size of civil infrastructure and their limited radio power has become apparent. Multi-hop communication protocols allow sen

Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Direct replacement of wired sensing systems with wireless sensor networks is not straightforward as off-the-shelf wireless systems are unlikely to provide the data users expect. The difficulty arises, in part, because centralized systems common for wired measurements are not scalable to large numbers of smart sensors. Decentralized computing

Smart sensors densely distributed over structures can use their computational and wireless communication capabilities to provide rich information for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Hardware resources available on smart sensors restrict data acquisition capabilities; intrinsic to these wireless systems are packet loss, data synchronizati

Smartphone based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed to evaluate International Roughness Index (IRI) based on dynamic responses of ordinary vehicles [1]. However, the robustness and accuracy were limited. In this paper, iDRIMS is improved mainly by employing frequency domain analysis. The algorithm consists of two steps. The first step is to identify the vehicle model and the second step is to estimate the IRI by utilizing the identified vehicle model. In the fi

Frequent and quantitative assessment of road condition is important as the maintenance of the road infrastructure needs to be performed with a limited budget. Vehicle Intelligent Monitoring System (VIMS) has been developed to estimate an index of road ride comfort (International Roughness Index; IRI) by obtaining the acceleration responses of ordinary vehicles together with GPS position data. VIMS converts the vertical acceleration of the measurement vehicle to acceleration RMS of the sprung mas