Given the ageing infrastructure, verifying the reliability of existing structures is crucial. Field testing presents a viable approach to evaluating a structure’s current condition, particularly proof load testing. In a proof load test, a large load is applied to assess its reliability. Structures in sound condition are expected to display satisfactory behaviour under average load intensities. Can good structural performance under moderate load levels already prove sufficient structural reliability? The proposed method utilises data from laboratory tests on similar structural elements. A case study was conducted on a bridge to illustrate the effectiveness of the method. Data acquired from laboratory tests were pre-processed to provide the required input for the reliability updating. It reveals that sufficient reliability can be demonstrated without excessive load levels by incorporating laboratory data. However, the actual capacity of the bridge and the uncertainty associated with the laboratory data remain important factors.

In the evaluation of existing bridges and viaducts, relying solely on a desk study is often inadequate for determining their structural reliability. Performing a proof load test provides valuable field data that offers detailed information about the structural integrity. However, the relation between the magnitude of the load and the structural reliability is not immediately clear. This study addresses the challenges associated with determining the target load and highlights the uncertainties that play a key role. A case study is presented that shows the time-dependent character of the structural reliability and the influence of an informative and a weakly informative prior distribution in a Bayesian context. It is shown how both past traffic loads and a proof load test may contribute to the proven strength of a structure. The described method provides a starting point towards a flexible approach for proof load testing in which structure-specific knowledge levels and requirements are considered.

Because of the aging of infrastructure, methods are explored by which the reliability of existing bridges and viaducts can be assessed. In cases in which limited information of the structure is available or its condition is of concern, proof load testing may be used to demonstrate sufficient live load carrying capacity. Proof load tests in the U.S.A. are typically performed using the Manual for Bridge Evaluation (MBE) published by the American Association of State Highway and Transportation Officials (AASHTO). The proof load is expressed by the regular live load model magnified by the target proof load factor. The level of reliability obtained using the target proof load factor is not explicitly stated in the MBE, but is of particular interest. In this article, relevant background documents are investigated to uncover the underlying calculations, assumptions, and input data. Current challenges in proof load testing are described in which the considerations of time dependence, stop criteria, available information, and system-level assessment are highlighted. Subsequently, improvements to the MBE proof load testing background are suggested. An example calculation using traffic data from the Netherlands shows that the HL93 load model and Eurocode LM1 provide a reasonably constant proof load factor with span length for bending and shear. However, the HS20 load model does not scale well with increasing span length. It is found that the magnitude of the target load as specified through the proof load factor is directly related to the desired level of reliability. Although the MBE proof load testing method is practical, several challenges remain.

The assessment of service-proven quay walls subject to corrosion-induced degradation is inherently a time-dependent reliability problem. Two major challenges are the modelling of corrosion and taking into account the decrease of epistemic uncertainty throughout the quay wall's service life. The main objective of this study is to examine the probability of failure, despite successful past performance, when the quay wall is subject to corrosion and randomly imposed variable loads. The development of the annual failure rate is modelled using crude Monte Carlo and by performing a first-order system reliability analysis. The annual failure rates found for service-proven quay walls vary over time. For those with successful service histories and subject to low corrosion rates, the highest reliability indices are observed in the first year of the service life, whereas with higher corrosion rates the final year prevails. In general, it seems more practical to evaluate reliability on an annual basis rather than over longer time periods, since the latter will introduce an iterative procedure to determine the wall's remaining lifetime. The key findings of this study can be crucial for the lifetime extension of existing quay walls, and presumably also for other service-proven geotechnical structures subject to corrosion.

General frameworks for reliability differentiation have evolved over time and are mainly developed for new buildings. However, recommendations for existing quay walls are lacking. In this study target reliability indices for assessing existing quay walls were derived by economic optimisation and by evaluating the Life Quality Index criterion (LQI). In quay wall design, some dominant stochastic design variables are largely time-independent, such as soil and material properties. The influence of time-independent variables on the development of the probability of failure was taken into consideration in this study, because this affects the present value of future failure costs and the associated target reliability indices. The reliability indices obtained in accordance with the LQI acceptance criterion were a little lower than the target reliability indices derived by economic optimization. The target reliability indices obtained for existing quay walls depend on the consequences of failure and the remaining service life. If failure modes of a quay wall are largely time-invariant and already survived the first period of the service life, the residual probability of failure is lower for an existing quay wall compared to a new quay wall. Hence, this should be considered in the determination of target reliability indices. The method of approach to assess the development of reliability over time can also be used for evaluating target reliability indices of other civil and geotechnical structures.

Design codes and standards rely on generalised target reliability indices. It is unclear, however, whether these indices are applicable to the specific risk-profile of marine structures. In this study, target reliability indices for quay walls were derived from various risk acceptance criteria, such as economic optimisation, individual risk (IR), societal risk (SR), the life quality index (LQI) and the social and environmental repercussion index (SERI). Important stochastic design variables in quay wall design, such as retaining height, soil strength and material properties, are largely time-independent, whereas other design variables are time-dependent. The extent to which a reliability problem is time variant affects the present value of future failure costs and the associated reliability optimum. A method was therefore developed to determine the influence of time-independent variables on the development of failure probability over time. This method can also be used to evaluate target reliability indices of other civil and geotechnical structures. The target reliability indices obtained for quay walls depend on failure consequences and marginal costs of safety investments. The results were used to elaborate the reliability framework of ISO 2394, and associated reliability levels are proposed for various consequence classes. The insights acquired were used to evaluate the acceptable probability of failure for different types of quay walls.