The adjustment of partial factors is a major lever that can be used when assessing an existing structure, avoiding unnecessary repairs and increasing the structural service life. A method to calibrate sensitivity factors, then fixed partial factors for a cluster of bridges is proposed herein. Different geometric configurations and uncertainties related to materials and loads are considered to create a collection of bridge samples. A probabilistic sensitivity analysis associated with a first order reliability method (FORM) is then performed to create a collection of most probable failure point (MPFP) coordinates. The objective is to align an assessment point with the MPFP as it is considered for design, though applying the approach to a particular cluster of bridges and including the ability to get some additional information on these structures and choices about target reliability level. Two approaches are compared to fit this new assessment point: (i) an averaged fixed sensitivity factor set matching some target reliability level and (ii) an optimization procedure enforcing reliability constraints for the whole collection of MPFP that provides fixed sensitivity factors associated with a bounded optimized failure point. Both approaches are applied to the generated samples of reinforced concrete frame bridges for illustration purpose.

In existing masonry buildings, the structural capacity with respect to horizontal actions is due to the in-plane response of shear walls, usually evaluated in terms of effective stiffness and drift capacity. The objective of this work is to provide first results to the damage sensitivity in diagonal cracking of URM shear walls and a study methodology based on vibration tests to assess the structural health of existing masonry buildings. The state of pre-existing discontinuities caused by previous damage events or material degradation may play a role in the in-plane response of shear walls. For this, an experimental campaign was carried out on three typologies of UnReinforced Masonry (URM) walls, simulating different damage situations, subjected to Shear-Compression (SC) and vibration tests in order to correlate the structural behaviour with changes in modal parameters. From the vibrational response acquired during the SC test, the modal parameters related to the pre-damage and post-damage condition were identified using the Continuous Wavelet Transform identification technique. The experimental results are then used to implement a Finite Element Model (FEM) with a 2D homogenization approach to simulate the behaviour of URM walls. A parametric study through FEM analysis is carried out in order to investigate the change in modal parameters for different damage configurations and for triggering strut mechanisms.

Autocorrelation is commonly used in signal processing for analyzing time signals, however the shape and magnitude of its statistical error, caused by its estimation from finite length measurements, is rarely addressed. Previous research showed that in the general case, the error on the autocorrelation estimator of an arbitrary signal is inversely proportional to the square root of the signal length. For operational modal analysis of linear systems under unknown ambient excitation, this error seems to present a peculiar shape influencing the estimation of damping ratios. In this paper the error on autocorrelation for a linear, time-invariant system subjected to white Gaussian noise is analytically and numerically studied. It is found to be very similar to the system's response to an ambient noise, thus with a frequency content close to that of the expected autocorrelation itself. An upper bound of error magnitude is found for each mode of the system. Then, the resulting error on damping computations is investigated through numerical simulations applied to a two degrees of freedom mechanical system. Numerical outcomes show good agreement with the theoretical part and are then supported by experimental data of a prestressed concrete bridge under ambient traffic. This study allows for a better understanding and quantification of the inaccuracy on damping ratios computation through techniques using autocorrelation in time domain, notably the logarithmic decrement or time–frequency domain decomposition based on wavelet analysis.

Providing a robust traffic management system is a given for road authorities today, for safer mobility and reliable day-to-day operations. Therefore, to improve traffic management procedures in order to provide an open road for road users at all times, understanding road surface friction measurement is crucial. Currently, the automatic prediction model of road surface friction measurement (FriC-PM) is highly demanded by road transport owners and operators. The prediction accuracy of road surface friction is linked to several external variables related to the weather conditions, road surface micro- and macro-texture, tire properties, driving speed and behaviour of road users. This paper presents a trained novel predictive model developed for the measurement of road surface friction considering a big dataset of 18 months with daily records through novel intelligent road-based passive sensor measurement, on a Spanish highway section. The trained predictive model is developed on the machine learning (ML) approaches, namely support vector machine (SVM), and validated with the K-Fold cross-validation (CV) algorithm taking into account various kernels. In addition, error evaluation strategies are applied to the trained model in order to identify the efficiency, accuracy, and reliability of the predictive model. The main purpose of the proposed prediction model is to provide comprehensive knowledge about predictive road surface friction coefficient, and to support decision-making elements to road owners or operators for reliable predictive maintenance process.

Changes in weather condition have a significant influence on the road surface friction. Extreme weather conditions are considered during heavy rains, and ice accumulation or snow-fall for the study of road surface. This could modify the road surface friction, a de-crease of the road surface friction due to extreme wet condition, and increase car accident rates. Weather condition related factors usually are water film thickness due to the rain, ice accumulation and major temperatures (pavement, ambient and freezing). Better under-standing of the weather condition changes on the road surface friction coefficient considering real-scale data monitoring system is essential to acclaim a reliable maintenance solution for road transport infrastructures. Long term data collection is a key factor for assess the relation between weather conditions, road surface friction coefficient. In the current re-search study, a preliminary assessment of weather condition influence on the road surface friction is elaborated for eighteen months considering data-driven from a novel intelligent road-based passive sensor. Results show that the most extreme weather conditions could impact road surface friction coefficient over time in road transportation system; mostly due to an increase of water film thickness and possibility of ice accumulation on the road surface. In conclusion different friction thresholds are elaborated for traffic management procedures, mainly 0.80 friction coefficient is considered the most stable and standard conditions, 0.60 acceptable, and 0.30 is an urgent condition.

In complex networks such as road infrastructure systems, disruptive events have direct consequences (loss of lives, structural damage, economic losses, etc.) related to the sub-system level directly affected, as well as indirect consequences on the overall system level, such as loss of functionality and related monetary consequences. To quantify the influence of component failures on network performance, complex network analysis makes it possible to represent the relation between its components and the consequences to estimate the functionality of the system. The serviceability of a road network is the possibility to use it during a given time period, and represents the performance of the system. Thus, time to restored serviceability after a disruptive event is a measure of road transportation resilience. In this paper, network analysis performed by the OSMnx Python package, supplied by road network data from OpenStreetMap, is applied to a Spanish motorway case study. The loss of serviceability index is obtained, combining the results of road links failure scenarios based on shortest paths and travel times to estimate road network resilience. The proposed methodology allows a suitable evaluation of the influence of road links in the risk assessment and management strategies by road infrastructure owners and managers.

Real prediction of friction coefficient on the road surface is essential in order to enhance the resilience of traffic management procedures for the safety of road users. Critical weather conditions could have a significant impact on the road surface, and decrease the reliable friction coefficient in extreme conditions. Weather parameters are involved in the process of traffic management are water film thickness, ice percentage, pavement temperature, ambient temperature, and freezing point. Smart road monitoring of the road surface friction changes over time means the real-time prediction of the friction coefficient changes in the future based on the intelligent weather road-based sensor is crucial to avoid uncontrolled conditions during extreme weather conditions. For this reason, the use of intelligent data analysis such as machine learning approaches is key in order to provide a holistic robust decision-making tool to support road operators or owners for further consideration of the traffic management procedures. In this study, a machine learning approach is applied to train 18 months of data collected from the real case study in Spain, and results show a good agreement between real friction coefficient and predicted friction coefficient. The trained model has been validated with various cross-validation approaches, and the high accuracy of the model is observed. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 769129 (PANOPTIS project).

This monitoring system aims at increasing the resilience of the road infrastructures and ensuring reliable network availability under unfavourable conditions, such as extreme weather, landslides, and earthquakes. The main target is to combine downscaled climate change scenarios (applied to road infrastructures) with simulation tools (structural/geotechnical) and actual data (from existing and novel sensors), so as to provide the operators with an integrated tool able to support more effective management of their infrastructures at planning, maintenance and operation level. Towards this, the proposed framework aims to use high resolution modelling data for the determination and the assessment of the climatic risk of the selected, transport infrastructures and associated expected damages, use existing SHM data (from accelerometers, strain gauges etc.) with new types of sensor-generated data (computer vision) to feed the structural/geotechnical simulator, utilize tailored weather forecasts (combining seamlessly all available data sources) for specific hot-spots, providing early warnings with corresponding impact assessment in real time; develop improved multi-temporal, multi-sensor UAV, computer vision and machine learning-based damage diagnostic for diverse transport infrastructures; design and implement a Holistic Resilience Assessment Platform environment as an innovative planning tool that will permit a quantitative resilience assessment through an end-to-end simulation environment, running “what-if” impact/risk/resilience assessment scenarios. The effects of adaptation measures can be investigated by changing the hazard, exposure and vulnerability input parameters; design and implement a Common Operational Picture, including an enhanced visualisation interface and an Incident Management System. The integrated platform (and its sub-modules) will be validated in two real case studies in Spain and in Greece.

In real structures, the proportional damping assumption is never strictly verified. Indexes of non-proportionality are then necessary to determine if this assumption leading to real modes still remains valid. If not, complex modes will appear and moreover, if their corresponding natural frequencies are close, their imaginary part can become large. In this paper, a new non-proportionality index, quantifying the "complexity" of mode shapes, is presented, derived from the notion of optimal complex modes introduced by Adhikari. This new index is designed for experimental results, for which the system's parameters are not known, and proven to be equal to the previous one up to the first order on damping. Modal identification based on wavelet analysis is considered promising in this study for processing free responses of non-proportionally damped systems, integrated in noise, to directly obtain complex modes. A procedure for choosing an appropriate quality factor for the time-frequency resolution, necessary to get correct identification results in the case of free responses combined with responses to ambient excitation and/or to additive noise, is detailed. The proposed identification technique based on Continuous Wavelet Transform (CWT) is finally applied on different transient responses of a masonry wall specimen during an experimental campaign comprising simultaneous vibrations and shear-compression tests. The results of the CWT method for modal identification are compared with those obtained by a classical modal analysis technique, called Least Squares Complex Frequency method, by means of the Modal Assurance Criterion and the proposed non-proportionality index. (C) 2021 Elsevier Ltd. All rights reserved.

Skid resistance is a significant feature that provides consistent traffic safety management for road pavements. An appropriate level of Skid resistance describes the contribution that the pavement surface makes to tire/road friction, and the surface of the road pavement can reduce vehicle operation cost, traffic accidents, and fatalities, particularly in wet conditions. Wet conditions decrease the level of the skid resistance (pavement friction), and this may lead to serious struggles related to driving on the road pavement (e.g., skidding or hydroplaning), which contributes to higher crash rates. The knowledge of skid resistance is essential to ensure reliable traffic management in transportation systems. Thus, a suitable methodology of skid resistance measurement and the understanding of the characterization of the road pavement are key to allow safe driving conditions. This paper presents a critical review on the current state of the art of the research conducted on skid resistance measurement techniques, taking into account field-based and laboratory-based methodologies, and novel road sensors with regard to various practices of skid resistance, factors influencing the skid resistance, the concept of the minimum skid resistance and thresholds. In conclusion, new trends that are relevant to data collection approaches and innovative procedures to further describe the data treatment are discussed to achieve better understanding, more accurate data interoperability, and proper measurement of skid resistance.

Maintenance and protection of road infrastructures (RI) against multi-hazard events requires the use of risk assessment to identify threats, to assess vulnerabilities and to evaluate the impact on infrastructure systems according to the probability of occurrence of such threats.Dealing with a complex network system, such as RI, demands the improvement of risk assessment approach by resorting to the concept of resilient infrastructure, capable of dealing with scenarios linked to disastrous events with the aim of minimizing service interruptions and quickly recovering.In this paper, the network analysis of a RI case study is addressed to obtain a qualitative measure of resilience related to travel patterns of road network.The topology of RI system, a pattern of connections according to graph theory, between components interacting jointly to provide the required functionalities, is supplied by road network data from OpenStreetMap.Then a probabilistic system model is used to define a non-degradated condition and the scenarios of failure events that can affect the network system.For each condition, the evaluation of the related shortest paths, as a measure of the network system performance, provide the qualitative resilience measurement identifying performance criteria or levels of service requirements.The proposed assessment through transport network measures can find application in risk assessment and management by RI owners and managers.

this paper analyses the numerical and experimental results to evaluate the dynamic properties of pultruded GFRP (Glass-Fiber Reinforced Polymers) buckled columns. The profiles are made of glass fiber reinforcement and thermosetting vinylester matrix with thin-walled open or closed cross section. The buckling phenomena of the column with fixed ends were evaluated with a non-destructive method based on experimental modal data through dynamic identification procedure. Numerical analysis has been carried out through Finite Element models calibrated considering two consecutive stages that involve the local and global scale: i) parametric natural frequencies analysis to model the different cross sections taking into account the stiffness of the rotational constraint between the wall segments of the thin walled pultruded profiles; ii) buckling analysis to identify the inaccuracies in the specimen or in the experimental apparatus through global flexural displacements which increase continuously with the axial load. Experimental, theoretical and numerical results were compared in order to know the wall segment effects of GFRP columns in free vibration field when affected by buckling phenomena. The results allow to investigate the significant role that the manufacturing imperfections of pultruded material play in the structural performance of GFRP buckled columns.

The preservation of architectural heritage from natural hazards and catastrophic events, including the seismic risk, requires in-depth investigation on structural behavior and in particular on the dynamic response of monumental and existing buildings, such as masonry structures. The support of experimental tests, dynamic identification techniques together with structural monitoring allow to a non-destructive evaluation of dynamic parameters and more in general for the seismic vulnerability assessment of heritage buildings, preserving its integrity and considering its operational status. In this paper, an experimental campaign on unreinforced masonry (URM) walls is presented on three typologies of walls considering induced damage. A methodology for the investigation of dynamic behavior of damaged URM walls proposed to support the most common dynamic identification techniques pointing out information through a simple spectral estimation.

L'objet de cette communication est la description de l'endommagement dans des milieux anisotropes tels que des murs en maconnerie ou la degradation mecanique peut etre fortement influencee par l'anisotropie elle meme. On utilise ici le cadre theorique de la mecanique continue de l'endommagement avec gradient. Ici ce gradient est oriente suivant un plan privilegie dans lequel, localement, l'endommagement sera le plus a meme de se propager. Pour accomplir cette tâche, on adopte la notion de base d'integrite qui, par ailleurs, permet une nette decomposition de la reponse mecanique en une partie directionnelle, une partie transversale, et une partie en cisaillement pur. Dans ce cadre, l'endommagement peut alors etre pilote par des deformations transversales au plan sus-cite, par des deformations de cisaillement suivant ce plan, ou par une combinaison des deux. En utilisant la terminologie employee en mecanique de la rupture, on peut alors parler de mecanismes en mode-I, en mode-II, ou en mode mixte I/II.

Compared with other diagnostic techniques, which are limited to the local investigation, the structural dynamic monitoring allows to obtain information about seismic response and vulnerability of structures, in their whole. The experimental modal analysis evaluates the dynamic parameters such as frequencies, vibration modes and damping coefficients. For historic buildings, due to their heterogeneity and complexity, these data are not yet readily available. The possibility of applying the simplified procedures for the dynamic identification of the different historic structural typologies is, therefore, strategic to obtain useful information to apply the design criteria and the structural verifications in the seismic field. Besides the simplified procedures allow an optimization both on the execution time and on the costs. The paper provides some hypothesis of simplified dynamic monitoring procedures through the reduction/optimization of the accelerometric sensors used for three case studies, which differ in structural typology such as Churches, Towers and Palaces.

After the seismic events of the 20th and 29th of May 2012 in Emilia (Italy), most of the monumental and historic buildings of the area were severely damaged. In a few structures, partial collapse mechanisms were observed (e.g. façade tilting, out-of-plane overturning of panels…). This paper presents the case-study of the bell tower of the Santa Maria Maggiore cathedral, located in Mirandola (Italy). The dynamic response of the structure was evaluated through operational modal analysis using ambient vibrations, a consolidated non-destructive procedure that estimates the dynamic parameters of the bell-tower. The dynamic tests were carried out in pre-intervention and post-intervention conditions in order to understand the sensitivity of dynamic measurements to safety interventions. Furthermore, a comparative study is made with similar cases of undamaged masonry towers up to the 6th mode. Finally, an investigation on the state of connections and of the building itself is carried out via FE model updating.