Decentralized identification and multimetric monitoring of civil infrastructure using smart sensors

Wireless Smart Sensor Networks (WSSNs) facilitates a new paradigm to structural identification and monitoring for civil infrastructure. Conventionally, wired sensors and central data acquisition systems have been used to characterize the state of the structure, which is quite challenging due to difficulties in cabling, long setup time, and high equipment and maintenance costs. WSSNs offer a unique opportunity to overcome such difficulties. Recent advances in sensor technology have realized low-cost, smart sensors with on-board computation and wireless communication capabilities, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing are common practice, WSSNs require decentralized algorithms due to the limitation associated with wireless communication; to date such algorithms are limited. This research develops new decentralized algorithms for structural identification and monitoring of civil infrastructure. To increase performance, flexibility, and versatility of the WSSN, the following issues are considered specifically: (1) decentralized modal analysis, (2) efficient decentralized system identification in the WSSN, and (3) multimetric sensing. Numerical simulation and laboratory testing are conducted to verify the efficacy of the proposed approaches. The performance of the decentralized approaches and their software implementations are validated through full-scale applications at the Irwin Indoor Practice Field in the University of Illinois at Urbana-Champaign and the Jindo Bridge, a 484 meter-long cable-stayed bridge located in South Korea. This research provides a strong foundation on which to further develop long-term monitoring employing a dense array of smart sensors. The software developed in this research is opensource and is available at: http://shm.cs.uiuc.edu/.NSF Grant No. CMS-060043NSF Grant No. CMMI-0724172NSF Grant No. CMMI-0928886NSF Grant No. CNS-1035573Ope

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Decentralized system identification using stochastic subspace identification for wireless sensor networks

Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model. ??? 2015 by the authors; licensee MDPI, Basel, Switzerlandopen0

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Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.ope

Principles and applications of ultrasonic-based nondestructive methods for self-healing in cementitious materials

Recently, self-healing technologies have emerged as a promising approach to extend the service life of social infrastructure in the field of concrete construction. However, current evaluations of the self-healing technologies developed for cementitious materials are mostly limited to lab-scale experiments to inspect changes in surface crack width (by optical microscopy) and permeability. Furthermore, there is a universal lack of unified test methods to assess the effectiveness of self-healing technologies. Particularly, with respect to the self-healing of concrete applied in actual construction, nondestructive test methods are required to avoid interrupting the use of the structures under evaluation. This paper presents a review of all existing research on the principles of ultrasonic test methods and case studies pertaining to self-healing concrete. The main objective of the study is to examine the applicability and limitation of various ultrasonic test methods in assessing the self-healing performance. Finally, future directions on the development of reliable assessment methods for self-healing cementitious materials are suggested.ope

Surface-Wave Based Model for Estimation of Discontinuity Depth in Concrete

In this paper, we propose an accurate and practical model for the estimation of surface-breaking discontinuity (i.e., crack) depth in concrete through quantitative characterization of surface-wave transmission across the discontinuity. The effects of three different mixture types (mortar, normal strength concrete, and high strength concrete) and four different simulated crack depths on surface-wave transmission were examined through experiments carried out on lab-scale concrete specimens. The crack depth estimation model is based on a surface-wave spectral energy approach that is capable of taking into account a wide range of wave frequencies. The accuracy of the proposed crack depth estimation model is validated by root mean square error analysis of data from repeated spectral energy transmission ratio measurements for each specimen

Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion

Displacement responses of a bridge as a result of external loadings provide crucial information regarding structural integrity and current conditions. Due to the relative characteristic of displacement, the conventional measurement approach requires reference points to firmly install the transducers, while the points are often unavailable for bridges. In this paper, a displacement estimation approach using Kalman filter-based data fusion is proposed to provide a practical means for displacement measurement. The proposed method enables accurate displacement estimation by optimally utilizing acceleration and strain in combination that have high availability and are free from reference points for sensor installation. The Kalman filter is formulated using a state-space model representing the double integration of acceleration and model-based strain-displacement relationship. The validation of the proposed method is conducted successfully by a numerical simulation and a field experiment, which shows the efficacy and accuracy of the proposed approach in bridge displacement measurement.ope

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Applicability of Diffuse Ultrasound to Evaluation of the Water Permeability and Chloride Ion Penetrability of Cracked Concrete

This study aims to explore the applicability of diffuse ultrasound to the evaluation of water permeability and chloride ion penetrability of cracked concrete. Lab-scale experiments were conducted on disk-shaped concrete specimens, each having a different width of a penetrating crack that was generated by splitting tension along the centerline. The average crack width of each specimen was determined using an image binarization technique. The diffuse ultrasound test employed signals in the frequency range of 200 to 440 kHz. The water flow rate was measured using a constant water-head permeability method, and the chloride diffusion coefficient was determined using a modified steady-state migration method. Then, the effects of crack width on the diffusion characteristics of ultrasound (i.e., diffusivity, dissipation), water flow rate, and chloride diffusion coefficient are investigated. The correlations between the water flow rate and diffuse ultrasound parameters, and between the chloride diffusion coefficient and diffuse ultrasound parameters, are examined. The results suggest that diffuse ultrasound is a promising method for assessing the water permeability and chloride ion penetrability of cracked concrete

Bayesian Prediction of Pre-Stressed Concrete Bridge Deflection Using Finite Element Analysis

Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. In addition, the probabilistic prediction model can be updated whenever additional measurement data is available. In this manner, a probabilistic prediction model, that is customized to a target bridge, can be obtained. The proposed method is applied to a pre-stressed concrete railway bridge in the construction stage in the Republic of Korea, as an example of a bridge for which accurate time-dependent deflection is difficult to predict, and measurement data are insufficient. Probabilistic prediction models are successfully derived by applying the proposed method, and the corresponding prediction results agree with the actual measurements, even though the bridge experienced large downward deflections during the construction stage. In addition, the practical uses of the prediction models are discussed