Assessment of shear strength of existing prestressed concrete bridge beams: Full-scale tests and numerical simulations

The obsolescence and the end of service lifetime of a big portion of our infrastructure, culmin- ated with recent catastrophic bridge collapses, determine an increasing need to implement monitoring activities and safety assessment of existing bridges and viaducts. As stated in numerous studies and experimental evi- dence, shear strength is one of the critical issues concerning existing bridges. In order to deepen the knowledge on structural shear behavior, an experimental campaign on full-scale prestressed concrete girders sampled from an existing structure has been performed in the structures laboratory of the University of Pavia (Italy). This study illustrates the results of experimental tests and numerical finite element simulations, which have been performed both to obtain a comparison with experimental data and to study the main contributing factors to the collapse of the girder for shear type failure. The failure strength obtained from experimental testing has been compared with the shear strength calculated according to different strength models presented both in design codes and in the literature; therefore the accuracy of such models will be assessed in order to define the most suitable approach to assess girder shear strength

Factor Analysis to Evaluate Hospital Resilience

Health care facilities should be able to quickly adapt to catastrophic events such as natural and human-made disasters. One way to reduce the impacts of extreme events is to enhance a hospital's resilience. Resilience is defined as the ability to absorb and recover from hazardous events, containing the effects of disasters when they occur. The goal of this paper is to propose a fast methodology for quantifying disaster resilience of health care facilities. An evaluation of disaster resilience was conducted on empirical data from tertiary hospitals in the San Francisco Bay area. A survey was conducted during a 4-month period using an ad hoc questionnaire, and the collected data were analyzed using factor analysis. A combination of variables was used to describe the characteristics of the hidden factors. Three factors were identified as most representative of hospital disaster resilience: (1)cooperation and training management; (2)resources and equipment capability; and (3)structural and organizational operating procedures. Together they cover 83% of the total variance. The overall level of hospital disaster resilience (R) was calculated by linearly combining the three extracted factors. This methodology provides a relatively simple way to evaluate a hospital's ability to manage extreme events

Modelling failure analysis of RC frame structures with masonry infills under sudden column losses

Robustness of structures is fundamental to limit progressive collapse of buildings in case of accidental loss of columns due to explosions, impacts or materials deterioration. Modelling of progressive collapse response of reinforced concrete (RC) frame structures needs considering extreme geometric and mechanical nonlinearities. Moreover, in the case of infilled frames the collapse mechanism becomes more complex because of the frame-infill interaction. This paper presents a numerical study aimed at proposing: A) an appropriate fiber-section modeling methodology for reinforced concrete frames under large displacement progressive collapse events; b) a new multi-strut fiber macro-element model to account for the influence of masonry infills in the progressive collapse response. Proposed numerical models are developed using the OpenSees software platform. The predictive capacity of the proposed methodology is widely validated in the paper through comparisons with experimental test results and refined numerical simulation pushdown test results. Results show that the new equivalent-strut modeling approach can be suitably employed as a simple assessment method when numerical simulation of progressive collapse scenarios is needed for bare and infilled reinforced concrete frames

Enhancing structural health monitoring with vehicle identification and tracking

Traffic load monitoring and structural health monitoring (SHM) have been gaining increasing attention over the last decade. However, most of the current installations treat the two monitoring types as separated problems, thereby using dedicated installed sensors, such as smart cameras for traffic load or accelerometers for Structural Health Monitoring (SHM). This paper presents a new framework aimed at leveraging the data collected by a SHM system for a second use, namely, monitoring vehicles passing on the structure being monitored (a viaduct). Our framework first processes the raw three-axial acceleration signals through a series of transformations and extracts its energy. Then, an anomaly detection algorithm is used to detect peaks from 90 installed sensors, and a linear regression together with a simple threshold filters out false detection by estimating the speed of the vehicles. Initial results in conditions of moderate traffic load are promising, demonstrating the detection of vehicles and realistic characterization of their speed. Moreover, a k-means clustering analysis distinguishes two groups of peaks with statistically different features such as amplitude and damping duration that could be likely associated with heavy vehicles and cars, respectively

Optimal design algorithm for seismic retrofitting of RC columns with steel jacketing technique

Steel jacketing (SJ) of beams and columns is widely employed as retrofitting technique to provide additional deformation and strength capacity to existing reinforced concrete (RC) frame structures. The latter are many times designed without considering seismic loads, or present inadequate seismic detailing. The use of SJ is generally associated with non-negligible costs depending on the amount of structural work and non-structural manufacturing and materials. Moreover, this kind of intervention results in noticeable downtime for the building. This paper presents a new optimization framework which is aimed at obtaining minimization of retrofitting costs by optimizing the position and the amount of steel jacketing retrofitting. The proposed methodology is applied to the case study of a 3D RC frame realized in OpenSEES and handled within the framework of a genetic algorithm. The algorithm iterates geometric and mechanical parameters configurations, based on the outcomes of static pushover analysis, in order to match the optimal retrofitting solution, intended as the one minimizing the costs and, at the same time, maintaining a specified safety level. Results of the proposed framework will provide optimized location and amount of steel-jacketing reinforcement. It is finally shown that the use of engineering optimization methods can be effectively used to limit retrofitting costs without a substantial modification of structural safety

Definition of out-of-plane fragility curves for masonry infills subject to combined in-plane and out-of-plane damage

The paper presents the outcomes of a probabilistic assessment framework aimed at defining out-of-plane fragility curves of unreinforced masonry infills walls which have been subjected (or not) prior in-plane damage. A recently developed in-plane (IP)/out-of-plane (OOP) four-strut macro-element model is used to model masonry infills within frames. Out-of-plane incremental dynamic analyses are performed, for a reference infilled frame, based on a suite of 26 ground motion record selection. Peak ground acceleration (PGA) and OOP relative displacement of the midspan node of the infill, are used as intensity measure and damage measure. The outcomes show fragility curves representing the probability of exceeding out-of-plane collapse at a given earthquake intensity as a function of a different combination of geometrical and mechanical parameters, in-plane damage level and supporting conditions. Results are finally summarized by curves relating in-plane interstorey drifts and out-of-plane average collapse PG

large scale monitoring system for existing structures and infrastructures

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A damage detection strategy on bridge external tendons through long-time monitoring

In recent years, Structural Health Monitoring (SHM) has gained a lot of attention, given the need to detect a structure damage at an early stage. A series of technological advances, especially in the world of new sensors, has allowed more structures to be equipped with an always increasing number of monitoring systems of different nature. The state of the art of monitoring systems involves the interaction and cooperation of elements such as low-cost sensors, efficient communication networks, data transfer and storage, often based on cloud architectures. If on the one hand the amount of data collected by the new SHM systems tends to be of considerable size, the search for damage passes through a process of information synthesis aimed at defining features able to describe the health of the monitored structure. Although many papers in literature are focused on the definition of an early warning through the most suitable damage feature, less attention has been paid till now to the challenge of implementing a fully automatic monitoring system that can serve as a robust and reliable tool for decision making. This paper presents a framework/architecture for a real-time data elaboration process, based on different alarm levels to track an ongoing and growing damage. Into details, data coming from a large number of MEMS accelerometers, installed on tensioning cables inside a box composite highway bridge, are continuously processed and analyzed at the sensor level. This is done on a microcontroller equipping each sensor, thanks to both fit-to-the-purpose algorithms that do not require huge computational effort and a strategy which can manage each sensor independently from the others. At a first stage, the proposed strategy is able to identify every kind of anomalies in the collected data; then, the benign phenomena, such as the occurrence of heavy though not extraordinary loading conditions, are identified and separated from those which clearly point at a damage, such as the breaking of the strands of prestressed cables. As these events occurred during the monitoring and have been recorded, we have a check about the capability of the chosen algorithms to perform this clustering. Different output examples are discussed in this paper in order to provide a significant case study where the effectiveness of a SHM system is discussed in a damage detection perspective

Structural health monitoring of a damaged operating bridge: Asupervised learning case study

The aging of materials, combined with the persistence and alteration of both operational loads and atmospheric conditions, cause a decrease of the structural properties of civil structures. This raises questions of considerable importance when it comes into play the safety of infrastructure users, mainly related to bridges and roads. Therefore, monitoring and evaluating the health of such structures becomes of central importance, allowing a more efficient maintenance, aimed at preserving or recovering the required structural properties. This article describes a case study where a structural health monitoring system has been installed on a damaged operating bridge, where the signs of heavy wear were first detected through visual inspections. Therefore, a network of accelerometers has been designed and installed to the purpose of monitoring the evolution of deterioration phenomena and testing some new approaches related to the use of MEMS sensors. In particular, sensors readings were collected in real-time in order to gain useful information about the dynamic behavior of the structure under ambient and traffic loads. Data obtained from the monitoring system were used to support the decision of carrying out maintenance operations aiming at reinforcing the bridge, increasing its structural stiffness. This result was achieved through the post tensioned reinforcement of the bridge, by means of external tendons. The vibration data were collected at different points along the bridge, before and after the maintenance operations, so that both the damaged and undamaged information are now known, suggesting a supervised learning approach for future monitoring of the structure. The modal parameters of the bridge, extracted from the data, have been used to verify the change in structural stiffness, confirming the effectiveness of the adopted intervention in improving the structural property