Journal of Civil Structural Health Monitoring.

Data-driven method of damage detection using sparse sensors installation by SEREPa Abstract This paper presents a model-based method of damage detection and severity identification in structural elements. The model-based method is performed with a finite element model. One of the important challenges in damage identification problems is lack of measured degrees of freedom and limitations of installed sensors on the structures. The new approach of this study is the use of expanded mode shapes data to train artificial neural network (ANN). In this study, the measured mode shapes are expanded by SEREPa. SEREPa expansion is developed based on the System-Equivalent Reduction and Expansion Process (SEREP), which is a non-smooth method and protects the measured data. ANN was then trained through the expanded data as inputs, location and severity of damage as outputs. The algorithm used to train ANN is scaled conjugate gradient. The advantage of this algorithm is that less data storage space is used and lower computation costs are needed. To show SEREPa’s efficiency in estimating unmeasured mode shapes, an experimental example containing a truss tower was presented. Two numerical examples including a plane truss and a space truss were presented to illustrate efficiency of damage detection method. Finally, the proposed method was verified by an experimental example. Damage prediction results for both numerical and experimental examples indicated an acceptable accuracy of the proposed method.

Application of infrared thermography for debonding detection in asphalt pavements Abstract Conventional testing practices in pavement damage surveillance and maintenance such as coring tend to be slow, destructive in nature and not wholly representative of the entire stretch of the pavement. Newly emerging technologies based on non-destructive testing techniques, do not demand implementing destructive measures or relying on extrapolation of point data and can be implemented after developing them into a proper technique and a set testing protocol. One such emerging technique that uses infrared emissions from any structure to inspect underlying defects is infrared thermography (IRT). In a patch of pavement containing subsurface defects, the heat flow to the surface of the pavement, which itself depends on the incident solar radiation, ambient temperature and other meteorological factors, gets disrupted causing a difference in the thermal signature of the defective and the sound patches. This difference in thermal signature can potentially be detected by a thermal imaging camera. This study aims at exploring the potential of IRT technique to assess the subsurface debonding defect in asphalt pavements. For this purpose, an in situ asphalt pavement test section has been constructed and different interlayer bonding conditions have been artificially induced in it. A novel way for quantitative and qualitative analyses of thermal signatures, using MATLAB for each of these bonding conditions has been carried out. The effect of different debonding agents on the overall thermal behavior of asphalt pavement has also been evaluated. Interchange time duration between day heating and night cooling cycles has been estimated, to determine the suitable time duration for asphalt pavement inspection. The results, advantages, and limitations of the method have been presented.

Use of fibre optic sensors for structural monitoring of temporary emergency reinforcements of the church S. Maria delle Grazie in Accumoli Abstract After natural disaster, damaged buildings need emergency reinforcements to recover to a stable structural condition. Since emergency reinforcements are components whose tension/compression is affected by the evolution of the static equilibrium of the supported structures, they can be instrumented to perform quasi-static structural monitoring. Fibre optic sensors based on the Bragg grating technology are well suited to instrument emergency reinforcement, as they allow easy installation, simple in-series cabling, and reliable long-term discontinuous monitoring with no unpredictable bias drifting in between successive monitoring campaigns. In this paper, we present an application on the early emergency reinforcements of the church Santa Maria delle Grazie in Colleposta, a small renaissance church damaged after the earthquake which interested central Italy in August 2016. As an emergency reinforcement, the church perimeter walls were confined by external wrapping. Sensors acting as strain gauges in temperature compensation regime were prepared custom, ready for installation on wrapping cables and along a vertical section of the wall. Results from monitoring campaigns in a 5-month time-span, seismic settling-down activity still being present, are presented, showing the easy installation and the effectiveness of the proposed procedure to monitor the working condition of the temporary emergency reinforcements.

Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building Abstract Artificial Neural Networks (ANN) have been proven applicable for updating finite-element (FE) baseline model and structural damage assessment. Most ANN-based damage identification methods use natural frequencies and mode shapes as input layer, limiting their application to quantifying single symmetrical damage in small structures. However, getting higher modal information of a structure is a crucial challenge in practice. As of late, researchers began utilizing mode shape derivatives as input layer in ANN to defeat the challenges for damage assessment in real-life structures. This study, therefore, proposes an ANN-based damage assessment method that employs the change in the first mode shape slope (CFMSS) damage index (DI) as input layer in ANN. For single-damage scenarios, the CFMSS-based DI has been able to detect, locate, and quantify the damage. For multiple-damage scenarios, the DI and corresponding stiffness reduction (SR) are fit as input and output layers, respectively, in ANN to measure the damage severity. Structural damage intensity is indicated as rate of decrease in story stiffness compared to baseline model. The efficiency of the proposed damage identification method is demonstrated through a nine-story numerical shear frame model and an experimental test on a three-story steel shear frame model.

Fiber optic sensors for high-temperature measurements on composite tanks in fire Abstract For the purpose of increasing payload and reduce freight cost, lightweight composite tank containers used for transportation have been progressively developed during the last years. Compared to conventionally produced cylindrical steel tanks, the fiber-reinforced solutions allow greater flexibility in the tank design. Despite a number of further material-related benefits of fiber-reinforced composites as non-conductive and non-magnetic behavior as well as corrosion resistance and high strength, the optimization of their thermal degradation properties during combustion is still a challenge. To improve the fire performance of lightweight composite containers, special intumescent fire protection coatings can be applied onto the outside tank surface. This paper presents fire tests on glass-fiber-reinforced plastic transport tanks with complex geometries sheltered with different surface-applied fire protection systems. To evaluate the fire resistance of the tank structures, a fiber optic monitoring system was developed. This system is based on distributed temperature measurements using high-resolution optical backscatter reflectometry and pointwise reference measurements using fiber Bragg gratings. Thereby, all the fiber optic sensors were directly integrated in the composite layer structure of the tanks. The focus of the presented work is on the demonstration of capability of fiber optic monitoring system in such high-temperature application. Moreover, the fiber optic measurements provide new insights into the efficiency of intumescent coating applied for fire protection of fiber-reinforced plastic transport tanks.

Damage assessment of an existing RC infilled structure by numerical simulation of the dynamic response Abstract This paper proposes a dynamic structural health monitoring and damage detection method to assess the structural integrity and safety of an RC framed building with masonry infills. The effectiveness of the method is verified by numerical analyses that were performed on a RC moment-resisting frame structure investigated by Paultre et al. and applied on an existing RC framed building with masonry infill walls, which provide a strong contribution to the dynamic response and seismic performance of the building. Different damage states are simulated through the nonlinear static (pushover) analysis developed by the FE model of the building, and this analysis considers both the bare framed structure and the infilled structure to appraise the damage to the infill walls. The identification and location of the damage is estimated using the modal properties of the damaged building at various steps of the nonlinear analysis. The results highlight the different evolutions of the damage in the bare and infilled frames and the possibility of detection via dynamic monitoring.

Sparse regularization-based damage detection in a bridge subjected to unknown moving forces Abstract Output-only structural damage detection (SDD) is an important issue in the field of structural health monitoring (SHM). As an attempt, this study aims to propose a sparse regularization-based method for detecting the structural damage using structural responses caused by unknown moving forces. First, a transmissibility matrix between two sensor sets is constructed using a known bridge model and least square-based moving force identification algorithm. Second, the measured responses are used as inputs to estimate the reconstructed responses with the help of the transmissibility matrix. Then, the damage detection procedure can be regarded as an optimization problem trying to find a possible damage vector, which makes the difference between the measured and reconstructed responses minimum. Lp-norm (0 < p ≤ 1) sparse regularization is adopted to improve the ill-conditioned SDD problem. To assess the feasibility of the proposed method, damaged bridges subjected to moving forces are taken as examples for numerical simulations. Differences between finite element model (FEM) used for model updating and the one applied to simulate the true damage conditions are considered. The illustrated results show that the proposed method can identify structural damages with a strong robustness. Some related issues, such as regularization parameters, finite element models, Lp-norm (0 < p ≤ 1) penalty terms, noise levels and damage patterns, are discussed as well.

Condition assessment of concrete by hybrid non-destructive tests Abstract This paper presents interim findings of a research project that was aimed to develop reliable methods to assess the condition of bridges and port-structures. The use of different types of non-destructive testing (NDT) equipment in assessing in situ concrete such as that for concrete cover measurement and locating the arrangement of the reinforcements, air permeability, electrical resistivity and the half-cell potential is reported in this paper. Six electrically inter-connected reinforced concrete (RC) specimens were made under laboratory conditions to validate NDT equipment output and to correlate them. Field testing was then conducted on the exterior of an RC wall of Doug McDonell Building at the University of Melbourne, Parkville campus. The results show that the use of multiple NDT equipment can enhance the understanding of the in situ condition by minimising the limitation inherent in each of the equipment. Combination of output from different testing methods allows a more precise condition assessment of the RC structure. In addition, measurements can be used to estimate the service life of the RC element.

Intelligent settlement monitoring system of high-speed railway bridge Abstract With the large-scale construction of high-speed railway bridges, monitoring their settlement has become an increasingly important task to ensure the safe operation of a high-speed railway. This paper presents a case study of a 100-km-long portion of an intelligent settlement monitoring system for the high-speed railway bridges (SMAIS) of the Beijing–Shanghai railway. The SMAIS consists of a sensor subsystem (SS), data acquisition and transmission subsystem (DAQ), data processing and analysis subsystem (DPA), database subsystem (DB), and data display and early warning subsystem (DEW). The SMAIS has the advantages of modularisation, low coupling, and interface standardisation. The general design principles of the SMAIS were studied, including its monitoring range calculation, monitoring point positions, and early warning threshold value determination. Proposals are made for the design of the SMAIS architecture and functions of its subsystems. The installation process and standard techniques for the SMAIS are also presented. The sensor precision test, temperature stability test, and general monitoring results are presented. An advanced continuous median data filtering-algorithm that considers the vehicle influence and error transmission is proposed.

Novel non-fiber optical metamaterial waveguide for monitoring canal and pipeline structures Abstract The electromagnetic metamaterials are the recent entrants, into the classification of smart materials for structural health monitoring (SHM). Their applications in SHM have raised the curiosity due to their rapid executable speeds in wide-frequency domains. The speed of obtaining health output signals for engineering structures is about 1/100th of the time taken by existing smart material-based frequency domain techniques such as piezoelectric material-based techniques. Recently, we developed an ultra-sensitive near-field sensing technique using metamaterial localized surface plasmon (LSP) ‘sensor’ which produced diagnosable ‘confined surface electromagnetic waves’ for SHM. This paper presents the same near-field sensing technique in the frequency domain but using metamaterial ‘waveguides’ based on propagating surface plasmon polaritons (SPPs)/surface waves. For the experiments, a novel robust metamaterial waveguide coupling zone was designed and applied for monitoring longitudinal and lateral displacements in civil engineering prototype structures such as channels and pipelines. In the context of SHM, coupling zone and metamaterial waveguide resemble fiber Bragg grating (FBG) sensor and optical fiber waveguide, respectively. Thus, if properly realized, these metamaterials can co-exist with the existing FBG/optical fiber techniques for applications in civil engineering.