G. W. van der Linden
Bio: G. W. van der Linden is an academic researcher from PDF Solutions. The author has contributed to research in topics: Structural health monitoring & Fixed wireless. The author has an hindex of 3, co-authored 4 publications receiving 115 citations.
TL;DR: The development of a novel wireless structural monitoring system specifically tailored for large-scale civil infrastructure systems by architecturally combining dense wireless sensor networks with a suite of information technologies remotely accessible by the Internet is reported on.
Abstract: Dense networks of low-cost wireless sensors have the potential to facilitate prolific data collection in large and complex infrastructure at costs lower than those historically associated w...
TL;DR: The development of a large-scale wireless structural monitoring system for long-span bridges is reported; the system is entirely wireless which renders it low-cost and easy to install.
Abstract: A dense network of sensors installed in a bridge can continuously generate response data from which the health and condition of the bridge can be analyzed. This approach to structural health monitoring the efforts associated with periodic bridge inspections and can provide timely insight to regions of the bridge suspected of degradation or damage. Nevertheless, the deployment of fine sensor grids on large-scale structures is not feasible using wired monitoring systems because of the rapidly increasing installation labor and costs required. Moreover, the enormous size of raw sensor data, if not translated into meaningful forms of information, can paralyze the bridge manager's decision making. This paper reports the development of a large-scale wireless structural monitoring system for long-span bridges; the system is entirely wireless which renders it low-cost and easy to install. Unlike central tethered data acquisition systems where data processing occurs in the central server, the distributed network of wireless sensors supports data processing. In-network data processing reduces raw data streams into actionable information of immediate value to the bridge manager. The proposed wireless monitoring system has been deployed on the New Carquinez Suspension Bridge in California. Current efforts on the bridge site include: 1) long-term assessment of a dense wireless sensor network; 2) implementation of a sustainable power management solution using solar power; 3) performance evaluation of an internet-enabled cyber-environment; 4) system identification of the bridge; and 5) the development of data mining tools. A hierarchical cyber-environment supports peer-to-peer communication between wireless sensors deployed on the bridge and allows for the connection between sensors and remote database systems via the internet. At the remote server, model calibration and damage detection analyses that employ a reduced-order finite element bridge model are implemented.
21 Jun 2011
TL;DR: In this paper, the authors describe an automated wireless structural monitoring system installed at the New Carquinez Bridge (NCB), which utilizes a dense network of wireless sensors installed in the bridge but remotely controlled by a hierarchically designed cyber environment.
Abstract: This paper describes an automated wireless structural monitoring system installed at the New Carquinez Bridge (NCB). The designed system utilizes a dense network of wireless sensors installed in the bridge but remotely controlled by a hierarchically designed cyber‐environment. The early efforts have included performance verification of a dense network of wireless sensors installed on the bridge and the establishment of a cellular gateway to the system for remote access from the internet. Acceleration of the main bridge span was the primary focus of the initial field deployment of the wireless monitoring system. An additional focus of the study is on ensuring wireless sensors can survive for long periods without human intervention. Toward this end, the life‐expectancy of the wireless sensors has been enhanced by embedding efficient power management schemes in the sensors while integrating solar panels for power harvesting. The dynamic characteristics of the NCB under daily traffic and wind loads were extra...
06 Aug 2013
TL;DR: A powerful data server called SenStore is described as the primary building block of the proposed cyberinfrastructure framework and its use in continuously monitoring the Telegraph Road Bridge (Monroe, MI) using a permanently installed wireless sensor network.
Abstract: Deployment of dense networks of low-power wireless sensors has been shown to be a cost effective approach to structural monitoring that can generate massive volumes of data. Building a specialized cyberinfrastructure system is an efficient way to store and organize large volumes of data (sensor and metadata) in addition to processing it. Availability of structure metadata (e.g., geometric details, material properties, inspection histories) further enhances the post-collection analysis of the data collected. In this paper, a comprehensive cyberinfrastructure and its associated computing technologies are proposed to serve as the backbone of large-scale permanent structural monitoring systems. A powerful data server called SenStore is described as the primary building block of the proposed cyberinfrastructure framework. This paper describes SenStore and its use in continuously monitoring the Telegraph Road Bridge (Monroe, MI) using a permanently installed wireless sensor network.
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.
TL;DR: This paper presents the results of a research project aimed at examining the capabilities and challenges of two distinct but not mutually exclusive approaches to in-service bridge assessment: visual inspection and installed monitoring systems.
Abstract: This paper presents the results of a research project aimed at examining the capabilities and challenges of two distinct but not mutually exclusive approaches to in-service bridge assessment: visual inspection and installed monitoring systems. In this study, the intended functionality of both approaches was evaluated on its ability to identify potential structural damage and to provide decision-making support. Inspection and monitoring are compared in terms of their functional performance, cost, and barriers (real and perceived) to implementation. Both methods have strengths and weaknesses across the metrics analyzed, and it is likely that a hybrid evaluation technique that adopts both approaches will optimize efficiency of condition assessment and ultimately lead to better decision making.
TL;DR: The state of the art in WSNs-based bridge health monitoring systems is reviewed including wireless sensor, network topology, data processing technology, power management, and time synchronization.
Abstract: Structural health monitoring (SHM) systems have shown great potential to sense the responses of a bridge system, diagnose the current structural conditions, predict the expected future performance, provide information for maintenance, and validate design hypotheses. Wireless sensor networks (WSNs) that have the benefits of reducing implementation costs of SHM systems as well as improving data processing efficiency become an attractive alternative to traditional tethered sensor systems. This paper introduces recent technology developments in the field of bridge health monitoring using WSNs. As a special application of WSNs, the requirements and characteristics of WSNs when used for bridge health monitoring are firstly briefly discussed. Then, the state of the art in WSNs-based bridge health monitoring systems is reviewed including wireless sensor, network topology, data processing technology, power management, and time synchronization. Following that, the performance validations and applications of WSNs in bridge health monitoring through scale models and field deployment are presented. Finally, some existing problems and promising research efforts for promoting applications of WSNs technology in bridge health monitoring throughout the world are explored.
TL;DR: Through a critical review of 485 articles, this paper investigates current data-driven bridge O&M decision-making in detail, including mainstream data types, issues related to data management, and typical application areas using these data.
Abstract: Bridges are critical infrastructure, and effective operation and maintenance (O&M) is essential for ensuring the good condition of bridges. Owing to the increasing complexity of modern bridges and ...
TL;DR: In this article, the authors presented the results of an ongoing research project conducted by the U.S. Federal Highway Administration (FHWA) on developing an intelligent approach for structural damage detection.
Abstract: This study presents the results of an ongoing research project conducted by the U.S. Federal Highway Administration (FHWA) on developing an intelligent approach for structural damage detection. The proposed approach is established upon the simulation of the compressed data stored in memory chips of a newly developed self-powered wireless sensor. An innovative data interpretation system integrating finite element method (FEM) and probabilistic neural network (PNN) based on Bayesian decision theory is developed for damage detection. Several features extracted from the cumulative limited static strain data are used as damage indicator variables. Another contribution of this paper is to define indicator variables that simultaneously take into account the effect of array of sensors. The performance of the proposed approach is first evaluated for the case of a simply supported beam under three-point bending. Then, the efficiency of the method is tested for the complicated case of a bridge gusset plate. The beam and gusset plate structures are analyzed as 3D FE models. The static strain data from the FE simulations for different damage scenarios is used to calibrate the sensor-specific data interpretation algorithm. The viability and repeatability of the method is demonstrated by conducting a number of simulations. Furthermore, a general scheme is presented for finding the optimal number of data acquisition points (sensors) on the structure and the associated optimal locations. An uncertainty analysis is performed through the contamination of the damage indicator features with different Gaussian noise levels.