Structural health monitoring of bridges : source localisation in acoustic emission technique

Managing the sustainability of urban infrastructure requires regular health monitoring of key infrastructure such as bridges. The process of structural health monitoring involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors, and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures, and acoustic emission is one technique that is finding an increasing use in the monitoring of civil infrastructures such as bridges. Acoustic emission technique is based on the recording of stress waves generated by rapid release of energy inside a material, followed by analysis of recorded signals to locate and identify the source of emission and assess its severity. This chapter first provides a brief background of the acoustic emission technique and the process of source localization. Results from laboratory experiments conducted to explore several aspects of the source localization process are also presented. The findings from the study can be expected to enhance knowledge of the acoustic emission process, and to aid the development of effective bridge structure diagnostics systems.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Seismic mitigation of building structural systems using passive dampers

This paper treats the seismic mitigation of medium rise frame-shear wall structures and building facade systems using passive damping devices. The frame shear wall structures have embedded viscoelastic and friction dampers in different configurations and placed in various locations in the structure. Influence of damper type, configuration and location are investigated. Results for tip deflections which provide an overall evaluation of the seismic response of the structure, are determined. Seismic mitigation of building facade systems in which visco-elastic dampers are fitted at the horizontal connections between the facades and the frame, instead of the traditional rigid connections, are also treated. Finite element techniques are used to model and analyse the two structural systems under different earthquake loadings, scaled to the same peak ground acceleration for meaningful comparison of responses. Results demonstrate the feasibility of these techniques for seismic mitigation.

Use of acoustic emission technique for structural health monitoring of bridges

Bridges are valuable assets of every nation. They deteriorate with age and often are subjected to additional loads or different load patterns than originally designed for. These changes in loads can cause localized distress and may result in bridge failure if not corrected in time. Early detection of damage and appropriate retrofitting will aid in preventing bridge failures. Large amounts of money are spent in bridge maintenance all around the world. A need exists for a reliable technology capable of monitoring the structural health of bridges, thereby ensuring they operate safely and efficiently during the whole intended lives. Monitoring of bridges has been traditionally done by means of visual inspection. Visual inspection alone is not capable of locating and identifying all signs of damage, hence a variety of structural health monitoring (SHM) techniques is used regularly nowadays to monitor performance and to assess condition of bridges for early damage detection. Acoustic emission (AE) is one technique that is finding an increasing use in SHM applications of bridges all around the world. The chapter starts with a brief introduction to structural health monitoring and techniques commonly used for monitoring purposes. Acoustic emission technique, wave nature of AE phenomenon, previous applications and limitations and challenges in the use as a SHM technique are also discussed. Scope of the project and work carried out will be explained, followed by some recommendations of work planned in future.

Structural dynamics research and applications

Research in structural dynamics has received considerable attention due to problems associated with emerging slender structures, increased vulnerability of structures to random loads and aging infrastructure. This paper briefly describes some such research carried out on i) dynamics of composite floor structure, ii) dynamics of cable supported footbridge, iii) seismic mitigation of frame-shear wall structure using passive dampers and iv) development of a damage assessment model for use in structural health modelling.

Investigation of hybridized polyurethane, glass fibre reinforced cement and steel laminate in structural floor plate systems

Sandwich components have emerged as light weight, efficient, economical, recyclable and reusable building systems which provide an alternative to both stiffened steel and reinforced concrete. These components are made of composite materials in which two metal face plates or Glassfibre Reinforced Cement (GRC) layers are bonded and form a sandwich with light weight compact polyurethane (PU) elastomer core. Existing examples of product applications are light weight sandwich panels for walls and roofs, Sandwich Plate System (SPS) for stadia, arena terraces, naval construction and bridges and Domeshell structures for dome type structures. Limited research has been conducted to investigate performance characteristics and applicability of sandwich or hybrid materials as structural flooring systems. Performance characteristics of Hybrid Floor Plate Systems comprising GRC, PU and Steel have not been adequately investigated and quantified. Therefore there is very little knowledge and design guidance for their application in commercial and residential buildings. This research investigates performance characteristics steel, PU and GRC in Hybrid Floor Plate Systems (HFPS) and develops a new floor system with appropriate design guide lines.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Prediction of structural integrity, robustness and service life using advanced finite element methods

Corrosion is a common phenomenon and critical aspects of steel structural application. It affects the daily design, inspection and maintenance in structural engineering, especially for the heavy and complex industrial applications, where the steel structures are subjected to hash corrosive environments in combination of high working stress condition and often in open field and/or under high temperature production environments. In the paper, it presents the actual engineering application of advanced finite element methods in the predication of the structural integrity and robustness at a designed service life for the furnaces of alumina production, which was operated in the high temperature, corrosive environments and rotating with high working stress condition.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Human activity-induced vibration in slender structural systems

Human activity-induced vibrations in slender structural sys tems become apparent in many different excitation modes and consequent action effects that cause discomfort to occupants, crowd panic and damage to public infrastructure. Resulting loss of public confidence in safety of structures, economic losses, cost of retrofit and repairs can be significant. Advanced computational and visualisation techniques enable engineers and architects to evolve bold and innovative structural forms, very often without precedence. New composite and hybrid materials that are making their presence in structural systems lack historical evidence of satisfactory performance over anticipated design life. These structural systems are susceptible to multi-modal and coupled excitation that are very complex and have inadequate design guidance in the present codes and good practice guides. Many incidents of amplified resonant response have been reported in buildings, footbridges, stadia a nd other crowded structures with adverse consequences. As a result, attenuation of human-induced vibration of innovative and slender structural systems very ofte n requires special studies during the design process. Dynamic activities possess variable characteristics and thereby induce complex responses in structures that are sensitive to parametric variations. Rigorous analytical techniques are available for investigation of such complex actions and responses to produce acceptable performance in structural systems. This paper presents an overview and a critique of existing code provisions for human-induced vibration followed by studies on the performance of three contrasting structural systems that exhibit complex vibration. The dynamic responses of these systems under human-induced vibrations have been carried out using experimentally validated computer simulation techniques. The outcomes of these studies will have engineering applications for safe and sustainable structures and a basis for developing design guidance.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Structural performance of reinforced concrete beams repairing from spalling

The effectiveness of a repair work for the restoration of spalled reinforced concrete (r.c.) structures depends to a great extent, on their ability to restore the structural integrity of the r.c. element, to restore its serviceability and to protect the reinforcements from further deterioration. This paper presents results of a study concocted to investigate the structural performance of eight spalled r.c. beams repaired using two advanced repair materials in various zones for comparison purposes, namely a free flowing self compacting mortar (FFSCM) and a polymer Modified cementitious mortar (PMCM). The repair technique adopted was that for the repair of spalled concrete in which the bond between the concrete and steel was completely lost due to reinforcement corrosion or the effect of fire or impact. The beams used for the experiment were first cast, then hacked at various zones before they were repaired except for the control beam. The beam specimens were then loaded to failure under four point loadings. The structural response of each beam was evaluated in terms of first crack load, cracking behavior, crack pattern, deflection, variation of strains in the concrete and steel, collapse load and the modes of failure. The results of the test showed that, the repair materials applied on the various zones of the beams were able to restore more than 100% of the beams’ capacity and that FFSCM gave a better overall performance.

Protection of structural systems and mechanisms from catastrophic and life-threatening failure caused by unforeseeable events

Structural framing systems and mechanisms designed for normal use rarely possess adequate robustness to withstand the effects of large impacts, blasts and extreme earthquakes that have been experienced in recent times. Robustness is the property of systems that enables them to survive unforeseen or unusual circumstances (Knoll & Vogel, 2009). Queensland University of Technology with industry collaboration is engaged in a program of research that commenced 15 years ago to study the impact of such unforeseeable phenomena and investigate methods of improving robustness and safety with protective mechanisms embedded or designed in structural systems. This paper highlights some of the research pertaining to seismic protection of building structures, rollover protective structures and effects of vehicular impact and blast on key elements in structures that could propagate catastrophic and disproportionate collapse.

Susceptibility of the critical structural components of railway bridges to the changes in train speed

Modern trains with different axle configurations, speeds and loads are used in railway networks. As a result, one of the most important questions of the mangers involved in bridge managements systems (BMS) is how these changes affect the structural behavior of the critical components of the railway bridges. Although researchers have conducted, many investigations on the dynamic effects of the moving loads on bridges, the influence of the changes in the speed of the train on the demand by capacity ratios of the different critical components of the bridge have not yet been properly studied. This study is important, because different components with different capacities and roles for carrying loads in the structure may be affected differently. To investigate the above phenomenon in this research, a structural model of a simply supported bridge is developed. It will be verified that the dynamic behavior of this bridge is similar to a group of railway bridges in Australia. Demand by capacity ratios of the critical components of the bridge, when it is subjected to a train load with different speeds will be calculated. The results show that the effect of increase or decrease of speed should not be underestimated. The outcome is very significant as it is contrary to what is currently expected, i.e. by reducing the speed of the train, the demand by capacity ratio of components may increase and make the bridge unsafe for carrying live load.