Proof load testing on bridges requires high magnitude loads. Stop criteria are used to avoid irreversible damage or failure during proof load testing. These stop criteria are thresholds to measurable parameters during the test. After reaching a stop criterion, the proof load test needs to be terminated. While in the past, stop criteria have been identified as a single level, this research proposes to use a traffic light system for stop criteria: green light (related to the serviceability limit state), yellow light (as an intermediate level) and red light (further testing is not permitted). The green light relates to the development of cracking, whereas the yellow and red light relate to the failure modes of flexure and shear. To develop stop criteria for the brittle failure mode of shear, thresholds are derived from mechanical models, based on strain measurements and crack widths, as well as using acoustic emission measurements. To validate the stop criteria, three series of experiments are analyzed: reinforced concrete slab strips, straight slabs, and skewed slabs. While field validation of the traffic light system is pending, the developed tool is a step forward to safely test concrete bridges without shear reinforcement.

Premature failures of in-service bridge expansion joints (BEJs) have become increasingly prevalent due to fatigue, traffic load, and environmental influences. The condition assessment of BEJs usually relies on the temperature-displacement correlation models using structural health monitoring (SHM) data for long-span bridges. However, such approaches are less applicable to small and medium bridges (SMBs), where the temperature–displacement relationship is not dominant, and the implementation of SHM systems is economically constrained. Consequently, routine assessment for SMBs remains largely dependent on manual inspection, which is labor-intensive and subjective. Acoustic-based monitoring is a promising and cost-effective solution for assessing damage severity and localization in BEJs, but its application to SMBs still remains limited. Moreover, existing studies mostly focus on deterministic models failing to quantify uncertainty, which is essential for trustworthy diagnostics under noise and variability. To address these limitations, this study proposes a probabilistic fault diagnosis framework based on convolutional neural networks with Bayesian deep ensemble (CNN-BDE) for BEJs of SMBs using acoustic signals. It incorporates an adaptive inter-class variance regularization term to enhance feature discrimination under noisy conditions. A Bayesian deep ensemble strategy is developed to quantify predictive uncertainty and improve the reliability of diagnostic results. Real-world acoustic data from in-service BEJs of SMBs are used to illustrate the feasibility of the proposed CNN-BDE. Compared to representative baseline methods under various working conditions, the results indicate that the proposed model achieves the highest diagnosis accuracy and best ability in uncertainty estimation.

The next generation of acoustic emission (AE) applications in concrete structural health monitoring (SHM) relies upon a reliable and quantitative relationship between AE measurements and corresponding AE sources. To achieve this, it is a prerequisite to accurately model the whole AE process that is a multiscale coupling process between local material fracturing and induced elastic wave propagation at structural level. Such a complex process, however, cannot be well addressed in currently available modelling methods. To fill this research gap, this study proposes a lattice modelling approach that achieves for the first time the explicit simulation of complete waveforms of transient AE signals induced by concrete fracture. The proposed approach incorporates an explicit time integration technique with a novel proportional-integral-derivative (PID) control algorithm for reducing spurious oscillations and a Rayleigh damping-based calculation and calibration method for the attenuation of AE waves. In this paper, the proposed lattice modelling approach is implemented to simulate the concrete Mode-I fracturing process in a three-point bending test. Besides the mechanical behaviors and AE hit number, a comparison was conducted between numerically and experimentally obtained AE waveforms. The AE waveforms and their attenuation characteristics simulated by the proposed lattice modelling method turn out to be comparable to experimental results. The proposed approach is of significance for a deep understanding of AE-related fracture mechanisms and a more reliable application of AE technique.

Transportation infrastructure demands reliable, cost-effective, environmentally friendly, and safe solutions. It is, therefore, crucial to leverage both the knowledge gained from current practices and the potential offered by emerging technologies. This paper uses the scoring system approached in the INFRACOMS project to offer a framework for asset managers and technology providers to identify areas of improvement and make informed decisions regarding selecting and implementing remote condition monitoring solutions. We focus on two technologies for bridges, like bridge weigh-in-motion and digital inspection and centre around four areas: data analysis, visualisation and integration and potential for practical decision-making. Technologies are evaluated based on their intended use, acknowledging that some may have multiple applications due to novel sensor installations or data interpretation/visualisation methods. Consequently, a technology may undergo multiple appraisals within this system. We showcase the benefits of the scoring system, alignment with specific use cases, and potential for broad applicability.

To date, there is no comprehensive approach available that can explicitly model the complete transient waveforms of acoustic emissions (AE) induced by fracture processes in brittle and quasi-brittle materials like concrete. The complexity of AE modelling arises from the intricate coupling between the local discontinuity of material fracturing and the global continuity of elastic wave propagation in solids. Among others, the lattice type models are promising approaches, as they are known to be a matured modelling approach to simulate the fracturing process in concrete-like materials. Nevertheless, like other discrete element methods (DEM), they are currently limited to describing the number and rate of AE events (broken elements) in the fracture process and cannot explicitly model wave generation and propagation. In this study, we propose a lattice modeling framework to simulate the propagation of complete waveforms of fracture-induced AE signals in concrete. A proportional-integral-derivative (PID) control algorithm is incorporated in an explicit time integration procedure to reduce dynamic noise from spurious oscillations. Additionally, a Rayleigh damping-based calculation method and corresponding calibration procedure are proposed to model the attenuation of AE signals due to material damping. Using the developed approach, we systematically investigate the feasibility of lattice models for elastic wave propagation simulation, the dependence of lattice mesh sizes and the choice of numerical damping parameters. These results lead to a systematic framework which can be employed in simulating wave propagation with attenuation using DEM models in general including lattice models.

Monitoring or identifying structural cracks is crucial for assessing the health of existing concrete structures. Key information about structural cracking encompasses the location of the crack and its kinematics, which include movements perpendicular and parallel to the crack face. Acoustic emission (AE) is a sensitive technique for detecting the location of internal concrete cracking. However, the state-of-the-art AE monitoring methods offer limited information on crack kinematics, restricting the use of AE in crack assessment. To bridge this gap, this paper uses a recently proposed AE data analysis method that quantifies the spatial distribution of AE events along a crack probabilistically. This method uses a parameter referred to as the probability density of AE events (pdAE). By combining pdAE and crack kinematics measured by digital image correlation in a series of real-scale concrete beam tests, this paper investigates the relationship between AE events and crack kinematics. The analysed cracks are generated by a combination of bending moment and shear forces, as commonly observed in real structural concrete members. We find that the amount of AE events is not only related to crack width (the crack movement perpendicular to the crack face), as most literature suggests, but also to the complete crack kinematics throughout the loading history of the member. We then provide a physical explanation for the observed relationships between concrete crack kinematics and the quantity of AE events.

Acoustic emission (AE) signal parameters can be used to classify the source type in concrete structures. However, signal parameters are influenced by the wave propagation from the source to the receiver, leading to wrong source classification results, especially for monitoring large concrete structures. This paper experimentally evaluates the influence of wave travel distance on signal parameters on a full-scale shear test of a reinforced concrete beam. The evaluated signal parameters include the RA value, average frequency, peak frequency, frequency centroid, and partial power. The evaluation reveals the limitation of using RA value - average frequency trends in large scale structural concrete members. Based on the evaluation, we propose a new source classification criterion using peak frequency or partial power, which can effectively classify the source type. The new criterion is also validated in a reinforced concrete slab test, which is another structural type. Based on the new criterion, we suggest a sensor layout that is suitable for source classification for large concrete structures. The results of this paper can help developing a reliable solution for real-time source classification for large concrete structures in general.

Advanced monitoring methods are required to identify stop criteria in proof-load tests. In this study, the combined methodology of two-dimensional digital image correlation and acoustic emission is investigated for its applicability for future implementation in field tests. The two monitoring systems are deemed to provide valuable insight with external measurements from digital image correlation and internal measurements from acoustic emission. Two overturned T-section reinforced concrete slabs (0.37 × 1.7 × 8.4 m) tested under laboratory conditions are used for the assessment. The first slab test served as a preliminary test to enable sensor placement and creation of a relevant loading protocol. The main scientific results lead to a proposal for a test procedure using the combined methodology based on results, observations, and experiences from an individual stop criteria assessment for the two methods. The results include full-field plots, an investigation of the time of crack detection and monitoring of crack widths with digital image correlation, and a qualitative assessment of activity vs. load followed by a quantitative evaluation of calm ratios using acoustic emission. The individual results show that both digital image correlation and acoustic emission can identify damage occurrence earlier than other secondary methods. At crack detection (415 kN), crack widths were measured at widths between 0.078 mm to 0.125 mm and can be monitored until reaching the stop criterion at 463 kN (Eurocode SLS threshold of wmax = 0.2 mm). The acoustic emission results were limited by the pre-defined loading protocol and thus, only indicated that damage occurred sometime between 300 kN and 500 kN (pre-defined load levels). Therefore, the proposal for test procedure involves a methodology, where the loading protocol may be updated during testing based on monitoring results and thus provide even more valuable data.

This paper proposes a new damage identification method, namely, the probability density field of acoustic emission (AE) events. This new method provides a different perspective to deal with the uncertainties in the source localization process. We treat the source location as a random variable, and estimate its probability density field based on a probability density function. The function was found from simulations where various uncertainties were included. The probability of AE events falling in a certain space range is the integral of the probability densities over that range. We apply the new method in a failure test of a full-scale reinforced concrete beam. The resultant probability density field clearly reflects the crack patterns of the specimen and a close relationship with the crack width.