Modal Sensitivity Based Sensor Placement for Damage Identification Under Sparsity Constraint

The present study deals with a comprehensive approach for damage identification of spatial truss structures. The novelty of the proposed approach consists of a three-level analysis. First, sensitivity of assumed modal characteristics is calculated. Second, natural frequency sensitivity is used to determine hardly identifiable structural parameters and mode shape sensitivity is applied to select damage-sensitive locations of sensors. Third, two sparsity constrained optimization algorithms are tested towards efficient identification of applied damage scenarios. These two algorithms are based on ℓ1-norm minimization and non-negative least square (NNLS) solution.Performances of both proposed algorithms have been compared in two realistic case studies: the first one concerned a three-dimensional truss girder with 61 structural parameters and the second one was devoted to an upper-deck arch bridge composed of 416 steel members

Structural behaviour and design criteria of under-deck cable-stayed bridges subjected to seismic action

SUMMARY: Under-deck cable-stayed bridges are very effective structural systems for which the strong contribution of the stay cables under live loading allows for the design of very slender decks for persistent and transient loading scenarios. Their behaviour when subjected to seismic excitation is investigated herein and a set of design criteria are presented that relate to the type and arrangement of bearings, the number and configuration of struts, and the transverse distribution of stay cables. The nonlinear behaviour of these bridges when subject to both near-field and far-field accelerograms has been thoroughly investigated through the use of incremental dynamic analyses. An intensity measure that reflects the pertinent contributions to response when several vibration modes are activated was proposed and is shown to be effective for the analysis of this structural type. The under-deck cable-stay system contributes in a very positive manner to reducing the response when the bridges are subject to very strong seismic excitation. For such scenarios, the reduction in the stiffness of the deck because of crack formation, when prestressed concrete decks are used, mobilises the cable system and enhances the overall performance of the system. Sets of natural accelerograms that are compliant with the prescriptions of Eurocode 8 were also applied to propose a set of design criteria for this bridge type in areas prone to earthquakes. Particular attention is given to outlining the optimal strategies for the deployment of bearings

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

Development of a Long-term, Multimetric Structural Health Monitoring System for a Historic Steel Truss Swing Bridge

The bridge stock across the United States is ageing, with many bridges approaching the end of their design life. The situation is so dire that the American Society of Civil Engineers gave the nation’s bridges a grade of “C+” in the 2013 edition of their Report Card on America’s Infrastructure. In fact, at the end of 2011, nearly a quarter of all bridges in the United States were classified as either structurally deficient or functionally obsolete. Thus, the nation’s bridges are in desperate need of rehabilitation and maintenance. However, limited funds are available for the repair of bridges. Management of the nation’s bridge infrastructure requires an efficient and effective use of available funds to direct the maintenance and repair efforts. Structural health monitoring has the potential to supplement the current routine of scheduled bridge inspections by providing an objective and detailed source of information about the status of the bridge. This research develops a framework for the long-term monitoring of bridges that leverages multimetric data to provide value to the bridge manager. The framework is applied to the Rock Island Arsenal Government Bridge. This bridge is a historic, steel truss, swing bridge that spans the Mississippi River between Rock Island, IL and Davenport, IA. The bridge is owned and operated by the US Army Corps of Engineers (USACE) and is a vital link for vehicular, train, and barge traffic. The USACE had a system of fiber optic strain gages installed on the bridge. As part of this research, this system was supplemented with a wireless sensor network that measured accelerations on the bridge. The multimetric data from the sensor systems was collected using a program developed in the course of this research. The data was then analyzed and metrics were developed that could be used to determine the health of the structure and the sensor networks themselves. Statistical process control methods were established to detect anomalous behavior in the short and long term time scales. Methods to locate and quantify the damage that has occurred in the structure once an anomaly has been detected were demonstrated. One of the methods developed as part of this research was a first order flexibility method. The SHM system this research develops has the desirable characteristics of being continuous temporally, multimetric, scalable, robust, autonomous, and informative. By necessity, some aspects of the developed SHM framework are unique and customized exclusively for the Rock Island Government Bridge. However, the principles developed in the framework are applicable to the development of an SHM system for any other bridge. Application of the SHM framework this research develops to other bridges has the potential to increase objectivity in the evaluation of bridges and focus maintenance efforts and funds on the bridges that are most critical to the public safety.Financial support for this research was provided in part by the Army Corps of Engineers Construction Engineering Research Laboratory (CERL) through a subcontract with Mandaree Enterprise Corporation.Ope

Application of the discrete element method for concrete fracturing.

This project focuses on discrete element modelling of fracturing of concrete material at meso-scale, and particularly on calibration of the particle assembly parameters to reproduce phenomenological properties of concrete, and on applying the discrete element method to analyze the failure mechanisms in a three-point bending test and debonding between the FRP sheet and the concrete. The particle flow code PFC2D and PFC3D are employed to carry out the parametric study but only PFC2D is used in the case studies. The calibration of properties of the numerical samples is conducted to determine the effects of the particle level input parameters on the elastic constants, the uniaxial compressive strengths and failure mode of particle assembly. The input parameters are divided into two groups, model constitutive parameters (e.g., particle and bond stiffness, bond shear and normal strengths and friction coefficient) and geometric and physical parameters (e.g., particle and specimen size, particle distribution and loading velocity.). The analysis is constructed using dimensional analysis and numerical uniaxial tests. A random aggregate generation algorithm is incorporated in the DEM code to reproduce the aggregate structure in real concrete material. The aggregate generation algorithm utilizes polygon and polyhedron as the basic shapes of aggregate and is capable of producing multi-graded concrete specimens with aggregate content up to 80% and 60% for two-dimensional and three-dimensional samples respectively. The mode I fracture behavior of three-phased concrete is then simulated by performing a virtual three-point bending test. The mortar matrix phase is simulated with the linear elastic-pure-brittle and softening bond model to ensure a fair comparison. The dynamic debonding process between the FRP sheet and the concrete is simulated with a particle assembly by a regular hexagonal packing arrangement where the heterogeneity of concrete is taken into account by incorporating the Weibull distribution. Based on the analysis of the modelling results, it is conclude that the fracture behavior of concrete can be satisfactorily captured by meso-scale DEM model and comprehensive parameter study allows more confidently implementation of particle flow code