Climate change is expected to alter the environmental factors that are known to influence the corrosion process, creating additional uncertainties in the long-term performance of reinforced concrete (RC) decks. With due consideration of site-specific exposure and environmental conditions, this study aims to investigate the degree to which projected climate change may impact corrosion-induced damage for RC bridges. A hierarchical two-tier framework was developed incorporating the material deterioration process simulation at the local element level, and a component level prediction of the corrosion-induced damage severity and extent over the bridge deck domain. The predictive accuracy of this framework was validated against the historical bridge inspection data. Case studies were performed for decks located in Toronto and Victoria to investigate the influence of climate data resolution and climate projection models on deck deterioration status. At last, ANN (artificial neural network) and SVM (support vector machine) approaches were used to generate a series of cartographic expressions to reveal how the corrosion-induced deck deterioration risk varies with the region due to the difference in the environmental conditions. These maps can serve as visual tools to express the corrosion damage risks for different bridge locations and to formulate region-based durability design requirements.

Thanks to the capabilities of distributed fibre optic sensors (DFOS), a significant increase in their applications within civil and geotechnical engineering has been observed during the last few years. In contrast to conventional spot gauges, the main advantage of this technology is direct damage detection, as the information is gained over the entire measurement path. This diagnostic solution is especially suitable for safety-critical infrastructure, including bridges. However, to utilise the benefits of the DFOS approach, appropriate interrogators, sensors, and installation methods must be adopted. The article briefly discusses possible solutions available on the market, indicating their specific advantages and limitations. Then, several practical case studies related to bridge monitoring in Poland are presented. The review shows various possible applications, from long measurement sections over entire spans to small-size structural components. Some are truly new and pioneer solutions, solving different technical problems in bridge engineering. Presented applications supported by in situ data proved the possible high-quality performance of DFOS-based systems for measurements of concrete, steel and composite bridges in actual operating conditions.

Due to several advantages, including controllable tolerance, accelerated construction and environmental friendliness, the grouting sleeve connections has been widely used in precast concrete pier structures. However, the seismic capacity of precast concrete piers would be significant impaired under the marine or deicing-salt environment. This article presents an experimental investigation on the seismic behaviour of corroded precast concrete piers with grouting sleeve connections. Five specimens with different corrosion levels were tested under cyclic loads. Seismic capacities of specimens with different corrosion levels, including hysteretic behaviour, stiffness degradation and energy-dissipation capacity, were analysed. The results showed that corrosion of reinforcements and grouting sleeve connections would significantly affect the seismic behaviour of the piers. The crack load, yield load and peak load decreased gradually with the increase of corrosion ratio, with the effect of corrosion on peak load being slightly greater than that on yield load. An approximate exponential decrease in yield displacement and peak displacement was observed as the corrosion ratio increased, and the decline rate in yield displacement and peak displacement gradually decreased. A numerical method for simulating the seismic behaviour of corroded precast concrete piers with grouting sleeve connections is proposed, and agreement between simulated and experimental results is observed.

Finite element analysis (FEA) has been used as a successful supplement to experimental testing in various studies for simulation of anchorage behaviour. Throughout the years, researchers have employed different modelling techniques in various FEA packages to capture the behaviour of post-installed anchors. However, the vast amount of knowledge accrued is yet to be reviewed. This article critically reviews all aspects of FEA from pre-processing to post-processing and provides a comprehensive review of published literature on FEA studies for predicting the behaviour of post-installed anchorage systems. Most current efforts focus on investigating failure mechanism of anchors in uncracked concrete under tensile loading. Findings show that developing finite element model for post-installed anchorage in concrete is very challenging due to complex geometrical configuration of anchors, difficulty in modelling concrete–anchor interface and lack of reliable information on selecting material properties and parameters. The analysis identified key gaps in research related to the effect of geometrical simplification, anchor subjected to dynamic loading and anchor performance in cracked concrete which needs attention in future research. This review article is a valuable resource in facilitating future research on assessing the performance of post-installed anchorage in concrete with FEA.

Structural resistance deterioration is by nature a non-increasing stochastic process with autocorrelation on the temporal scale. The Gamma process is often used to describe the stochastic behavior of resistance deterioration. With Gamma-based deterioration models, the calculation of time-dependent reliability presents a serious challenge, and often Monte Carlo simulation is the only solution to this problem. This paper derives a closed-form solution for time-dependent reliability of aging structures, in which the resistance deterioration is described by a Gamma process and the applied load is modelled as a Gaussian process with a constant standard deviation. The accuracy of the proposed method is verified through a comparison with Monte Carlo simulation results, and its applicability is further illustrated in a time-dependent reliability analysis of an existing highway bridge whose vehicle load is modelled using weigh-in-motion (WIM) data. It is found that the measured vehicle load has a relatively small uncertainty, and the uncertainty associated with resistance deterioration is crucial to the reliability assessment because it dominates the overall uncertainty in the reliability calculation.

Recognising the criticality of highway bridges for the socio-economic development of a nation, multiple researchers have endeavoured towards quantifying the seismic fragility of these key infrastructure elements. Given the present boom in urbanisation, however, there may also be an appreciable likelihood of vehicle presence on the highway bridge deck during an earthquake event. This study focuses on the impact assessment of vehicle-bridge-interaction (VBI) on the seismic fragility of highway bridges by explicitly accounting for vehicular movement atop the bridge deck during seismic shaking. Addressing existing drawbacks in literature that typically adopts a simplified modelling strategy and static vehicle placement, this paper considers different types of moving vehicles (modelled as mass-spring-dashpot system analogies) for VBI assessment through a novel algorithm. The proposed framework also accounts for uncertainties involved in the moving vehicle analysis, pertaining to vehicle characteristics and driver’s perception of the earthquake. The framework is demonstrated on representative multi-span continuous steel girder bridges located in Central and Southeastern United States. A comparative assessment of hysteretic response of bridge components and seismic fragility curves indicate a considerable influence of vehicular movement on bridge damage, signifying the importance of considering moving VBI effects for thorough seismic assessment of highway bridge structures.

Conceptual pre-estimation of road tunnel costs is a vital yet challenging process during feasibility studies due to the prevailing underground uncertainties and risks. Most existing cost estimation approaches are deterministic, probabilistic methods are limited to a single cost examination. This paper introduces a probabilistic model for pre-estimating road tunnels life-cycle costs. It aims to capture their inherent uncertainty holistically, thus enabling more reliable decision-making at a project’s early stages. The proposed model is developed in three steps: multiple regression analysis, fitting distribution, and Monte-Carlo simulation. The first step unveils the correlations between independent and dependent variables, while the other two steps return the probabilistic descriptions of cost drivers and life-cycle costs. Civil engineering, electromechanical works costs, energy consumption, and operation & maintenance costs are determined as the model’s output variables. A real-world database of 32 dual-bore road tunnels with a total length of 55 km is used to develop probability distributions within reasonable confidence intervals and a sensitivity analysis was performed utilizing tornado charts. The results can assist public authorities and managers in estimating the probability of a specific life-cycle cost and making appropriate decisions in accordance with their risk tolerance.

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

This study analytically investigated the axial-flexural (P-M) interactions of quare reinforced concrete (RC) columns discontinuously strengthened swith fibre reinforced polymers (FRP). Six FRP confined concrete stress-strain models and two sectional analysis methods were used to develop the analytical P-M interactions of discontinuously FRP strengthened square RC columns. The developed analytical P-M interactions were validated against experimental data available in the literature. It was found that the selection of the stress-strain models had a substantial effect on the analytical P-M interactions, while the influence of the sectional analysis methods heavily depended on the selected stress-strain models. It was also found that the three available design codes for concrete structures externally strengthened with FRP yielded different analytical P-M interactions of discontinuously FRP strengthened square RC columns. A parametric analysis was undertaken and found that the corner radius and confinement ratio significantly affected the P-M interactions of discontinuously FRP strengthened square RC columns. Also, discontinuously FRP strengthened square RC columns with a higher number of FRP layers and smaller corner radius obtained lower flexural and axial strengths than their counterparts with a lower number of CFRP layers and higher corner radius.

Hydrodynamic models are used to analyse water networks (water distribution, drainage, surface water, district heating, etc.). The non-linear nature of water flows necessitates the use of iterative solution methods in hydraulic modelling. This requires a relatively large computational effort. To reduce this effort, networks, network forcing and/or the flow in networks are often simplified and analysed using the Graph Theory. The simplification options depend on the network characteristics. There are many topological features to describe Graph-based networks. In this paper, these characteristics are summarised, applied on 7 urban drainage networks and discussed. As the topological features do not describe the networks in a uniform manner, a new type of topological characterisation of looped drainage networks (Network Linearisation Parameter, NLP) is proposed based on linearized hydraulics and bottlenecks identified in paths to outfalls.