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The fatigue safety assessment of critical details in steel bridges is dominated by several uncertainties mainly related to load modelling. The use of integrated weigh-in-motion (WIM) systems in roadway bridges allows to collect all the necessary information for tracking actual traffic conditions. Based on WIM data, the uncertainty associated to the evaluation of the probability of fatigue failure is reduced, allowing the optimal scheduling of repair and maintenance activities and thus improving safety and economic savings. The paper presents a methodology for estimating fatigue life cycle cost (LCC) of roadway bridges equipped with a WIM system that considers uncertainties in measurement and modelling errors. The procedure allows the quantification of the economic gain enabled by WIM systems, useful in assessing the cost/accuracy trade-off for a given WIM system. It is found that the continuous use of a WIM system may improve the rate of decrease of the structural reliability index, compared with temporally limited use of WIM data. The periodic update of the reliability index and of the probability of failure based on WIM measurements results in a rather accurate estimation of the fatigue life cycle cost, which is crucial in identifying the payback time of the WIM system. [ABSTRACT FROM AUTHOR]

This paper presents the application of a Structural Health Monitoring (SHM) strategy for bridges affected by slow deformation phenomena to a curved roadway bridge undergoing slow landslide‐induced movements. The chosen multidisciplinary approach is based on the combination of remote sensing data through satellite Synthetic Aperture Radar Interferometry (InSAR) and structural analysis up to collapse investigated through the Applied Element Method (AEM). The results of the application to the considered case study demonstrate that the integration of the displacement information obtained through InSAR technique with the numerical analysis allows for improving the comprehension of the health state of the bridge undergoing slow movements and identifying potentially critical conditions for the structure. [ABSTRACT FROM AUTHOR]

This paper presents the structure of new collaborative research project entitled SAFOTEB "A reviewed SAfety FOrmat for structural reliability assessment of post‐TEnsioned concrete Bridges". The research aims at assessing the reliability of the current safety format and developing specific suggestions for existing concrete bridges with post‐tensioned cables. To date, no European code provides specific guidance for such a purpose. Indeed, the status of conservation and possible defects of the cables may be hidden, enlarging the uncertainties in the structural assessment of such constructions. The basic steps of the research are: (i) definition of the probabilistic models for the main variables concerning the assessment of existing post‐tensioned prestressed concrete bridges; (ii) reliability assessment and calibration of updated safety coefficients for the partial factors method; (iii) evaluation of the residual life and model updating through real‐time continuous monitoring systems. The full procedure will be applied on existing case study structures used as benchmarks. The project is funded by FABRE, an Italian research consortium composed by universities and research institutions aimed at the evaluation and monitoring of bridges, viaducts, tunnels and other structures. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health‐state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early‐stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population‐level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage‐sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health‐state inference for SHM applications with sparce datasets. [ABSTRACT FROM AUTHOR]

The assessment of risk conditions of bridges and viaducts allows management authorities to ensure the safety of the users and the proper allocation of economic resources for maintenance and retrofitting interventions. A common circumstance in practice regards the lack of information as well as the need to update it, causing delays in the risk evaluation and, consequently, in the entire decision‐making process. Especially in the managing of large bridge assets, the retrieval/updating of information can be costly, both in terms of time and economic resources. In this regard, the execution of visual inspections to determine the defect level of bridges and viaducts is the most resource‐intensive operation on the part of the management authorities. In this paper, a new methodology to prioritize visual inspections of bridges according to their risk condition and operating costs is proposed and illustrated with a real case study. A novel aspect of the proposed methodology regards the use of information gain criteria to refine the outcomes from the risk assessment and prioritization process. Finally, the outcomes of the methodology are implemented in a GIS framework to ensure high interoperability and facilitate the construction of intuitive risk maps to assist inspection plans. [ABSTRACT FROM AUTHOR]

The behavior of post‐tensioned concrete bridges is often influenced by time‐dependent phenomena, such as relaxation of prestressing steel, creep and shrinkage of concrete. For bridges built using the balanced cantilever method, the evolution of time‐dependent phenomena during the construction phase must be carefully considered. In common practice, the assessment of the structural safety of this bridge typology is a complex task due to the numerous factors and uncertainties involved in the evaluation process. Numerical models can be adopted to perform structural analyses at successive phases over bridges' lifetime, while destructive and non‐destructive evaluation methods are used to gather information to ensure that the numerical model matches the actual behavior of the bridge. In this context, this paper presents the structural behavior assessment of a multi‐span post‐tensioned box girder bridge with vertically prestressed internal joints. A numerical model was constructed to investigate on the effect of creep and construction phases on the actual stress/strain conditions of the bridge. To reduce model‐based uncertainties, compression tests were carried out on concrete cores extracted from the structure and the results of static load tests were used for model validation purposes. [ABSTRACT FROM AUTHOR]

Bridges' integrity, safety and serviceability are fundamental targets that need to be ensured during the structural lifetime, owing to the crucial role that such infrastructure components play in modern communities. Recently, a particular attention is being paid to the management of bridges since many of them have reached the end of their expected service life. To this purpose, the Italian Ministry of Sustainable Mobility issued in 2020 the "Guidelines for risk classification and management, safety assessment and monitoring of bridges" that regulate visual inspections, risk assessment based on multilevel protocols and consequent monitoring and maintenance activities. In this context, structural health monitoring (SHM) systems play a fundamental role to guarantee safety and adequate performance levels. However, the synergy between visual inspections and SHM is yet to be fully understood and exploited. The main goal of this research work is to contribute to filling this gap by proposing a general probabilistic framework, based on Life‐Cycle Cost Analysis (LCCA), able to fuse information from SHM data and visual inspections, with the aim to plan over time short‐term and long‐term interventions and to optimize fund allocation by considering all the stored information. [ABSTRACT FROM AUTHOR]

Recent advances in computing performance and simulation tools allow today the development of high-fidelity computational models which accurately reproduce the structural behavor of existing structures. At the same time, advancements in sensing technology and data management enable engineers to remotely observe monitored structures in a continuous and comprehensive way. Merging the two approaches is a challenge recently addressed by the engineering research community, which led to the concept of digital twin (DT)—a simulation model continuously fed by sensor data which, throughout the whole lifespan of the structure, stands as its digital proxy. In the seismic field achieving such a task is still problematic, in particular for large and complex structures such as historical masonry palaces. To this aim, the paper proposes the integrated use of DTs and vibration data to support the seismic structural health monitoring of monumental palaces, discussing a practical application to the historical Consoli Palace in Gubbio, Italy. To overcome the computational limitations of classical approaches, an efficient equivalent frame (EF) model of the palace is built and continuously updated in quasi real-time based on modal information identified from vibration data. The performance and accuracy of the Equivalent Frame model are compared with those of a high-fidelity Finite Element representation, highlighting both their feasibility and limitations. Employing modal data recorded across the 15 May 2021 earthquake, the EF model demonstrates the ability to quickly assess the structural integrity of the palace in the post-earthquake scenario, as well as to forecast the residual capacity with respect to future seismic events. [ABSTRACT FROM AUTHOR]

Over the last decades, the rising number of aging infrastructures has progressively fueled much interest toward the field of structural health monitoring. Following the increasing popularity of artificial intelligence algorithms, an autoencoder‐based damage detection technique within the context of unsupervised learning is proposed in this paper to provide support for practical engineering applications. The developed methodology uses the autoencoder to reconstruct raw acceleration sequences of user‐defined length collected from a healthy structure. To quantify the errors between the original input and the reconstructed output, which may be representative of damage occurrence, two indexes of reconstruction loss are selected as damage‐sensitive features. To support damage detection, a selected number of short‐time sequences are finally grouped into a unique macrosequence. The novel procedure can effectively both work at the single sensor level, as well as combine the predictive models using an ensemble learning strategy. Avoiding system identification, results obtained in the Z24 bridge demonstrate that the proposed method is quite effective for local damage detection with limited computational effort and using a limited number of sensors, thereby suitable to be easily applicable in the context of real‐time bridge assessment. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR) using satellite data is revealing a promising tool for monitoring long-term deformation phenomena in critical infrastructural systems. Nevertheless, its use in structural engineering is still quite limited and a general understanding of its potential is still missing, especially when dealing with bridge structures for which specific methods of data processing and displacement assessment with error quantification need to be developed, accounting for the type of deformation phenomena and for the orientation of the bridge. In order to partly fill this research gap, this paper proposes a post-processing methodology to derive two-dimensional displacement configurations of multi-span bridges with properly defined error bounds, using both ascending and descending Synthetic Aperture Radar acquisitions. In order to obtain an engineering meaningful estimate of the uncertainties affecting the reconstructed bridge deformations, both random and systematic errors are quantified, accounting for the orientation of the bridge with respect to the Line-Of-Sights of the satellites, the hypothesized deformation plane and the accuracy of InSAR measurements. The proposed procedure has been applied to the illustrative case study of the Albiano-Magra Bridge in Italy, collapsed on 8 April 2020. The results, referred to the monitoring period 2015–2020, demonstrate the effectiveness of the proposed method in supporting engineering assessments. In particular, an initially temperature-induced stationary deformation phenomenon has been observed, with all spans moving upwards or downwards during summer and winter. Afterwards, displacements of increasing amplitude for two side spans have been observed during the three years preceding the failure, providing information on the possible cause of collapse. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of rebars' corrosion monitoring systems on the vulnerability assessment and life-cycle cost analysis (LCCA) of reinforced concrete (RC) bridge piers subjected to repeated earthquake loading. A methodology is proposed to investigate the life-cycle economic savings gained with the use of a structural health monitoring (SHM) system made of corrosion sensors, exploiting the construction of damage-dependent fragility curves and using the pacific earthquake engineering research (PEER) equation to calculate the seismic probability of failure. The availability of corrosion sensors' data is accounted for in the degradation model for assessing the probability of failure by assuming that the recorded corrosion parameters are deterministic variables during the monitored period. Monitoring data are simulated by choosing values from the literature for the parameters representing the corrosion conditions. Seismic damage accumulation is considered through Monte Carlo (MC) simulations of long-term seismic scenarios considering the mean annual rate of earthquake occurrence obtained by the seismic hazard curves. The results of the methodology application to a case study highlight the importance of incorporating corrosion effects for the correct estimation of seismic life-cycle vulnerability. They are also used to discuss the advantage in terms of the safety and economy of including corrosion sensors for optimizing maintenance activities as opposed to purely inspection-based evaluations. [ABSTRACT FROM AUTHOR]

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake. [ABSTRACT FROM AUTHOR]

Tall buildings are especially prone to damage caused by winds and earthquakes. In practice, only a single hazard is assumed to dominate the design and is adopted for structural verifications. This is also the case when supplemental damping devices such as tuned mass dampers (TMDs) are adopted. Nevertheless, previous research has shown that from a life-cycle cost (LCC) perspective the dominant hazard concept is misleading, and a multi-hazard approach is necessary. This article proposes a methodology for multi-objective optimization-based design of multiple TMDs (MTMDs) attached to tall buildings subjected to both winds and earthquakes. The LCC related to damage on non-structural components is taken as one of the objective functions to join consistently these hazards. The MTMDs initial cost is selected as the second objective function. The results of these multi-objective optimization problems are Pareto fronts of optimized solutions that may be of interest to stakeholders, including non-technical decision makers. A genetic algorithm is adopted as solution strategy. A realistic case study is presented, and the optimization results are compared with classic literature solutions. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) based on Automated Operational Modal Analysis (A-OMA) has gained increasing importance in the conservation of heritage structures over recent decades. In this context, finite element model updating techniques using modal data constitute a commonly used approach for damage identification. Nevertheless, the large number of simulations usually involved in the associated minimization problem hinders the application to real-time condition assessment. This is especially critical for historic buildings, where the modelling of complex geometries involves large computational burdens. Alternatively, surrogate models offer an efficient solution to replace computationally demanding numerical models and so perform continuous model updating. In this light, this paper presents a surrogate-based model updating approach for online assessment of historic buildings and its application to a medieval masonry tower, the Sciri Tower in Perugia (Italy). Using modal properties identified by A-OMA, the proposed approach allows the continuous fitting of certain damage-sensitive parameters of the structure. To do so, three different surrogate models are considered, including the quadratic response surface method, Kriging, and Random Sampling High-Dimensional Model Representation, and their effectiveness is compared from an SHM perspective. The reported results demonstrate the suitability of the proposed methodology for tracking the temperature-dependent intrinsic properties of the tower. [ABSTRACT FROM AUTHOR]