The first, third and fourth authors also acknowledge that, this work was partly 574 financed by FEDER funds through the Competitivity Factors Operational 575 Programme - COMPETE and by national funds through FCT Foundation for 576 Science and Technology within the scope of the project POCI-01-0145-FEDER- 577 007633. This project has received funding from the European Union’s Horizon 578 2020 research and innovation programme under grant agreement No 769255. This 579 document reflects only the views of the author(s). Neither the Innovation and 580 Networks Executive Agency (INEA) nor the European Commission is in any way 581 responsible for any use that may be made of the information it contains.

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Comparison of forecasting models to predict concrete bridge decks performance

Predictive analytics of bridge safety for intelligent transportation system using ensemble model

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called ‘two-step cluster analysis’ is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. 

Bridge deterioration models for different superstructure types using Markov chains and two-step cluster analysis

The ability to assess the risks of the functional safety of railway tracks allows harmonizing characteristics of track elements under certain operating conditions under certain maintenance for the efficient use of the track structure throughout its life cycle. The concept of detailing conditions of the interaction of the rolling stock and railway track was used for the productive solution of tasks of infrastructure functional safety assessment. The paper formed an approach to the analytical solution of determination problems of deformability parameters over time using the elastic waves theory. The formation method of interconnections between the technical and economic aspects of the operation of railway infrastructure was proposed. The criteria of deformability work and intensity of use were utilized for the effective use of the track structure through its life cycle. The results of calculations are presented to assess changes in the deformability behaviour of the track elements and structure when the force and speed parameters of the operating conditions change, as well as the algorithm of the method for estimating the operation deformability of the railway track. Thus, the proposed approach can be adapted to optimize objects by railway functional safety assessment at the stage of object operation simulation.

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Using Detailing Concept to Assess Railway Functional Safety

Natural disasters are unavoidable and can cause serious damage to bridges, which may lead to catastrophic losses, both human and economic. Therefore, the assessment of bridges exposed to these events is of paramount importance to identify possible mitigation needs. The objective of the present work is to present consistent tools that may allow us to obtain the failure probability of a masonry arch bridge under a flood event, leading to local scour. Surrogate models were implemented to ease the computational cost of the probabilistic analysis. Moreover, a stochastic parametric analysis based on the geotechnical properties of the soil components of masonry arch bridges located in Portugal was performed. The results show the failure mechanism of the masonry arch bridges when subjected to scour-induced settlements and the influence of soil density on the failure probability obtained for different flow discharge values and angles of attack. The presented methodology and derived fragility curves can be used to assess bridge performance under a flood event, thus providing useful information for bridge management and monitoring.

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In-Plane Fragility and Parametric Analyses of Masonry Arch Bridges Exposed to Flood Hazard Using Surrogate Modeling Techniques

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Overview on performance predictive models – Application to Bridge Management Systems

Keeping transport links open in adverse conditions and being able to restore connections quickly after extreme events are important and demanding tasks for infrastructure owners/operators. This paper is developed within the H2020 project SAFEWAY, whose main goal is to increase the resilience of terrestrial transportation infrastructure. Risk-based approaches are excellent tools to aid in the decision-making process of planning maintenance and implementation of risk mitigation measures with the ultimate goal of reducing risk and increasing resilience. This paper presents a framework for quantitative risk assessment which guides an integrated assessment of the risk components: hazard, exposure, vulnerability and consequences of a malfunctioning transportation infrastructure. The paper guides the identification of failure modes for transportation infrastructure exposed to extreme events (natural and human-made) and provides models for and examples of hazard, vulnerability and risk assessment. Each assessment step must be made in coherence with the other risk components as an integral part of the risk assessment.

Risk Assessment of Terrestrial Transportation Infrastructures Exposed to Extreme Events

Bridges have a significant importance within the transportation system given that their functionality is vital for the economic and social development of countries. Therefore, a high level of safety and serviceability must be achieved to guarantee an operational state of the bridge network. In this regard, it is necessary to track the performance of bridges and obtain indicators to characterize the evolution of structural pathologies over time. In this paper, the time-dependent expected deterioration of bridge networks is investigated by use of Markov chains models. Bridges in a network are likely to share similar environmental conditions but depending on their functional class may be exposed to different loading conditions that diversely affect their structural deterioration over time. Moreover, the deterioration rate is known to increase with time due to aging. Hence, it is useful to identify and divide the bridge network into classes sharing similar deterioration trends in order to obtain a more accurate prediction. To this end, data mining tools such as two-step cluster analysis is applied to a dataset obtained from the National Bridge Inventory (NBI) database, in order to find associations among the bridge characteristics that could contribute to build a more specific degradation model which accurately explains and predicts the future condition of concrete bridges. The results demonstrate a particular deterioration path for each cluster, where it is evidenced that older bridges and those having higher Average Daily Traffic (ADT) deteriorate faster. Therefore, the degradation models developed following the proposed methodology provide a more accurate prediction when compared to a single degradation model without clustering analysis. This more reliable models facilitate the decision process of bridge management systems.

Stochastic Degradation Model of Concrete Bridges Using Data Mining Tools

Life-Cycle Analysis is usually referred to as the assessment of a system that includes the three pillars of sustainability, i.e. economic, environmental, and social aspects. Many works have been developed presenting and discussing methodologies and frameworks to include the evaluation of these aspects. In general, economic performance is the most addressed, followed by the environmental aspect. A highly generalized formulation is usually used regarding social aspects since it corresponds to the society at large. The present work is focused on the economic aspects of the assessment, implementing a life-cycle cost analysis methodology to an infrastructure system (viaduct) covering all direct costs for agency/owners. Furthermore, to indirectly account for climate changes, and the requirements of the quality control plan (QCP) for operational issues, a semi-probabilistic approach is carried out. The life cycle cost analysis assumes a uniform probabilistic distribution for the intervention time of maintenance and rehabilitation activities. With this, variations in the degradation processes due to climate changes and/or intervention needs to fulfill requirements from QCP, are considered. For this purpose, the time at which each intervention might occur in the future is assumed as a random variable. Monte Carlo simulation is then used to compute the cost of several different scenarios. On other hand, the present work provides another approach referred as the deterministic approach. It estimates the life cycle costs deterministically, and considers at the end of the analysis the uncertainties associated with the life-cycle process in a rough way assuming a general coefficient of variation of ±20%. The main goal of this work is to understand the impact that uncertainties in the intervention schedule might have on the final life-cycle cost.

Life-Cycle Cost Analysis of a Viaduct Considering Uncertainties on the Interventions Plan

Bridges present valuable assets for the rail and road network by providing cross at critical links such as waterways, valleys, and other types of facilities. However, these types of structures are exposed to several threats during their life-cycle such as natural hazards and deterioration, which compromise their performance. To assess the condition state of such infrastructure and define maintenance and mitigation strategies, several performance indicators of quantitative nature have been proposed during the last decades by several researchers. Among those indicators, risk has received great attention as it enables to account for both the performance of infrastructures subjected to hazard events, and the consequences associated to an inadequate level of service of the infrastructure. Nevertheless, risk is not a stationary indicator, i.e. several parameters involved in the estimation of risk are time-dependent. One of them comprises the structural capacity of infrastructures, which is affected by deterioration effects over time. This gradual deterioration can be regarded as an interceptable hazard, which may act simultaneously with other non-interceptable hazards such as natural events (e.g. earthquakes). Therefore, a risk assessment framework should account for the probability of having these multiple hazards acting during the service life of infrastructures. The aim of this paper is to conduct a risk assessment for a railway bridge subjected to a multi-hazard scenario, i.e. an observable interceptable hazard corresponding to chloride induced corrosion of the reinforcing steel in reinforced concrete elements, together with seismic hazard. The results of the study demonstrate the relevance of considering time-dependent deterioration effects on the risk assessment of bridges, as the increase in the seismic fragility over time is significant. These findings are relevant for decision-making to plan and execute optimal interventions.

Monica Santamaria

Papers

Overview on performance predictive models – Application to Bridge Management Systems

Risk Assessment of Terrestrial Transportation Infrastructures Exposed to Extreme Events

Stochastic Degradation Model of Concrete Bridges Using Data Mining Tools

Life-Cycle Cost Analysis of a Viaduct Considering Uncertainties on the Interventions Plan

Risk Assessment of a Railway Bridge Subjected to a Multi-hazard Scenario