Structural Health Monitoring (SHM) systems produce large amounts of time-series data. But raw sensor-data may present poor quality due to measurement errors including anomalous data, irregular sampling, round-off-errors, and missing data. These measurement errors distort the sensor values and lead to poor data reliability, and limit the applicability of data-driven models. Moreover, they might include a combination of effects such as temperature-induced variability , which must be separated for further inferences. Poor data quality thus poses a significant challenge to the reliability of SHM analyses, and this challenge is amplified by the scale of the data, emphasizing the need for low-cost, automated data-cleaning solutions.

To address this challenge, we develop a plug-and-play pre-processing framework. This tool removes anomalous values using ensemble models and separates temperature-induce response variability from the intended response via regression schemes and external meteorological sources. Moreover, it addresses irregular measurements by resampling data and accounts for missing data by adaptive imputation techniques. The developed framework is validated by applying it to a case-study bridge, demonstrating its practical utility and scalability.

Routine visual inspections of bridges are safety-critical activities that are still manual, time-consuming, and subjective to inspector’s interpretation. This paper presents a scalable automated visual inspection pipeline for bridge defect segmentation, targeting the 19-class dacl10k benchmark dataset. We perform the first systematic comparison of pretraining paradigms on dacl10k – CNN-supervised, ViT-supervised, masked image modelling, and self-supervised learning – across 10 configurations under a controlled training recipe. Pretraining paradigm consistently dominates architectural choice, with DINOv2-L pretrained on 142 million unlabelled images achieving 49.16% mean Intersection-over-Union (mIoU). Applying our native multi-label training approach to EVA-02-L, the dacl10k challenge winning backbone, achieves 48.97% mIoU versus their 47.80% single-model result, demonstrating that training design is an independent performance factor. A three-model ensemble achieves 51.08% mIoU, exceeding the challenge winner using half the number of models. A prototype inspection system (InSpectralytiX) is deployed as a HuggingFace Gradio Space demonstrating end-to-end feasibility from raw image to per-class defect map. The future work targets automated condition scoring for bridge asset management integration supporting structural health monitoring at local level when performed repeatedly.

Adaptive service life prediction of reinforced concrete structures requires continuous, real-time assessment of the chloride-induced depassivation phase. Within this study, an established probabilistic service life model can be combined with embedded corrosion wire sensor data to enable adaptive, data-driven lifetime prediction under real exposure conditions. An integrated corrosion monitoring system with embedded filament wire sensors was employed in the instrumented prestressed concrete research bridge Concerto. These wire sensors enable direct, real-time detection of the depassivation front by measuring the abrupt increase in electrical resistance upon wire fracture. The resulting depassivation times are interpreted and incorporated as input parameters in an established probabilistic lifetime model. By updating model parameters based on wire sensor data, uncertainties are reduced and predictions are adapted to the actual structural condition. Evaluation of the long-term measurements from the Concerto research bridge demonstrates its applicability under real exposure conditions and illustrates how sensor-derived depassivation data can be integrated into established probabilistic lifetime models as a basis for data-driven maintenance planning and reliable estimation of remaining service life. Adaptive service life prediction of reinforced concrete structures requires continuous, real-time assessment of the chloride-induced depassivation phase. Within this study, an established probabilistic service life model can be combined with embedded corrosion wire sensor data to enable adaptive, data-driven lifetime prediction under real exposure conditions. An integrated corrosion monitoring system with embedded filament wire sensors was employed in the instrumented prestressed concrete research bridge Concerto. These wire sensors enable direct, real-time detection of the depassivation front by measuring the abrupt increase in electrical resistance upon wire fracture. The resulting depassivation times are interpreted and incorporated as input parameters in an established probabilistic lifetime model. By updating model parameters based on wire sensor data, uncertainties are reduced and predictions are adapted to the actual structural condition. Evaluation of the long-term measurements from the Concerto research bridge demonstrates its applicability under real exposure conditions and illustrates how sensor-derived depassivation data can be integrated into established probabilistic lifetime models as a basis for data-driven maintenance planning and reliable estimation of remaining service life.

Corrosion is the main deterioration process affecting reinforced concrete, and it can lead to a loss of serviceability and a reduction in structural reliability over time. The probabilistic model for corrosion initiation is now well established, and for practical applications, chloride ingress into concrete is usually described by Fick’s second law of diffusion. In this model, the chloride content at a given depth and exposure time depends on the surface chloride concentration and the apparent diffusion coefficient. The model parameters and the time of corrosion initiation are uncertain and vary with environmental exposure and concrete properties. Reducing the uncertainty in these parameters is therefore important to improve the prediction of the time to corrosion initiation. In this study, a Bayesian Network framework is proposed to refine the uncertainty in the model parameters by combining information from three different types of tests: (i) destructive tests on core samples providing a chloride content profile at three depths, (ii) non-destructive tests, using half-cell potential measurements, which provide an indication about the state of corrosion and (iii) continuous monitoring data, which provide information about the initiation time by detecting the change in electrochemical response when chlorides reach the reinforcement level. The evidence from these three monitoring strategies is used to update the probability distributions of the model parameters and the time of corrosion initiation.

The management of aging steel bridges is posing significant challenges to the bridge owners, who must maintain their structural integrity under fatigue induced deterioration at critical locations such as welds while operating within limited maintenance budgets. Traditional methods utilize fixed-term prescriptive measures, such as inspections conducted at fixed intervals and qualitative evaluation which can introduce structural risks and occasionally surpasses the budgetary constraints resulting in the inefficient usage of precious resources. This study presents a risk informed decision approach by integrating probabilistic reliability analysis, with adaptive inspection and interventions strategies to quantify and extend the operational service life of fatigue prone steel bridges. The proposed approach will comprise three main components; a) Structural reliability and adaptation with measures (system state actions) b) Structural performance: Structural failure and damage probabilities multiplied with benefits, costs and consequences for these events c) and decision and identifying the most cost- and risk-efficient measures based on cost reduction, risk mitigation and service life extension. This approach will provide bridge engineers and managers with decision support towards cost efficient and sustainable bridge management and informed decision making.