Abstract
Most snow avalanches occur unobserved, which becomes particularly dramatic when human lives are involved. Seismological observations can be helpful to unravel time and dynamics of unseen ...events, like the deadly avalanche of January 18, 2017, that hit a Resort-hotel at Rigopiano in the Abruzzi (Italy). Particle motion analysis and spectrograms from data recorded by a close seismic broadband station, calculation of synthetic seismograms, as well as simulation of the flow, allowed us to construct the dynamics of the snow avalanche that buried alive 40 people, killing 29. Due to the bad weather conditions, no visual observation was made, thus making it impossible to determine the exact moment of the avalanche and to report necessary observations of the dramatic event. On-site inspections revealed that the hotel was horizontally cut by shear forces and dislocated by 48 m in 70°N direction, once the increasing avalanche pressure exceeded the structural shear strength of the building. Within an eligible 24 min time range of the avalanche, we found three weak seismic transients, starting at 15:42:38 UTC, recorded by the nearest operating station GIGS located in the Gran Sasso underground laboratory approximately 17 km away. Particle motion analysis of the strongest seismic avalanche signal, as well as of the synthetic seismograms match best when assuming a single force seismic source, attacking in direction of 120°N. Simulation of the avalanche dynamics—calculated by using a 2D rapid mass movement simulator—indicates that the seismic signals were rather generated as the avalanche flowed through a narrow and twisting canyon directly above the hotel. Once the avalanche enters the canyon it is travelling at maximum velocity (37 m/s) and is twice strongly deflected by the rock sidewalls. These impacts created a distinct linearly polarized seismic “avalanche transient”s that can be used to time the destruction of the hotel. Our results demonstrate that seismic recordings combined with simulations of mass movements are indispensable to remotely monitor snow avalanches.
Complexity and robustness of frame structures De Biagi, Valerio; Chiaia, Bernardino
International journal of solids and structures,
10/2013, Letnik:
50, Številka:
22-23
Journal Article
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In structural engineering, the word “complex” is often employed to define something that is difficult to understand or to solve. Anyhow, even if the concept is extensively employed in the practice, ...any proper definition has not been formulated yet. Taking one’s cue from other scientific disciplines, a metric for complexity based on the performance of the load paths through the structural scheme is proposed. Referring to frames, characterized by large interaction between the beams, an analysis based on graph theory is proposed. A beam importance factor is then deduced and preliminary robustness properties are discussed. Three examples are proposed in order to examine the capabilities of the complexity indices and the beam importance factor. The structural robustness is then worked-out. Load redistribution, which represents an important aspect in structural behavior, should act in the structure in such a way that a damage would not cause collapse. The effects of a prescribed damage on a frame are analyzed and commented.
The study of progressive collapse and structural robustness has advanced significantly after 9/11 event. There is a growing interest in the phenomenon, as well as in the development of numerical and ...experimental techniques that have led to great progress in understanding the structural robustness and integrity. However, the general ideas, concepts and definitions have been merely changed over the past twenty years. These concepts and definitions are first developed in the framework of a threat-independent methodology, implicitly focused on blast-induced progressive collapse (or other short-term extreme events) in framed structures, and then, generalized to other structural types, mechanisms and triggering events, without scrutinization. In this paper, the current definitions of the terms progressive collapse, initial (local) damage and progressive collapse analysis are challenged, their insufficiency is discussed and possible improvements are provided. The suggested definitions and discussions provide a deeper and more general nomenclature for progressive collapse and related topics.
In 2020, the Italian Guidelines for the classification, assessment and management of bridges were published, introducing a novel multilevel approach to assess the degree of risk of existing bridges ...at the national scale. Even if these guidelines give, for the first time in Italy, a comprehensive approach to the assessment and management of bridges, in some cases, detailed procedures are missing. One of these cases is represented by the R.C. half-joint, a kind of deck/girder connection that was commonly used in the past. This joint deserves particular attention in the assessment since in many cases it is subjected to deterioration and the relevant reinforcement is not properly designed for the lack of specific provisions in the old Italian technical standards. In this scenario, this article aims to propose a specific methodology for the assessment of half-joints that is consistent with both the above-mentioned Italian Guidelines and the current Italian Technical Standard. However, this methodology can be used as a reference also in other countries since most of the relevant provisions are general whilst the detailed rules are Eurocode based. In this article, first the type of investigations to be performed to reach a proper knowledge level is defined, describing possible destructive and non-destructive tests. Then, a procedure for numerical verification of the joint is described. This procedure, based on the adoption of three different Strut-and-Tie Models is introduced and deeply analysed, highlighting the limits and the benefits of each model. Finally, the application of this procedure to a case study is shown.
Bridge authorities have been reticent to integrate structural health monitoring into their bridge management systems, as they do not have the financial and technical resources to collect long-term ...monitoring data from every bridge. As bridge authorities normally own huge amount of similar bridges, like the pedestrian ones, the ability to transfer knowledge from one or a small group of well-known bridges to help make more effective decisions in new bridges and environments has gained relevance. In that sense, transfer learning, a subfield of machine learning, offers a novel solution to periodically evaluate the structural condition of all pedestrian bridges using long-term monitoring data from one or more pedestrian bridges. In this paper, the applicability of unsupervised transfer learning is firstly shown on data from numerical models and then on data from two similar pedestrian prestressed concrete bridges. Two domain adaptation techniques are used for transfer learning, where a classifier has access to unlabeled training data (source domain) from a reference bridge (or a small set of reference bridges) and unlabeled monitoring test data (target domain) from another bridge, assuming that both domains are from similar but statistically different distributions. This type of mapping is expected to improve the classification accuracy for the target domain compared to a procedure that does not implement domain adaptation, as a result of reducing distributions mismatch between source and target domains.
In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional ...methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.
Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is ...aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.
► To measure the eco-mechanical performances of concrete, new indexes are introduced. ► Compressive behaviour defines the mechanical properties of concrete. ► CO2 released by cement and fibers ...production is evaluated. ► Two self-consolidating mixtures, with and without fibers, are investigated. ► In the best SCC, cement is replaced by FA and SF and steel fibers are added.
The environmental performances of concrete structures have been largely investigated in the last years. However, mechanical behavior and ecological aspects are not always combined in these analyses. Thus, two low-carbon footprint self-consolidating concretes have been subjected to uniaxial compression loads, in order to measure the strength and the dissipated energy during failure. Starting from these tests, new eco-mechanical ratios are herein introduced with the aim of tailoring eco-friendly concrete with acceptable mechanical performances. As a result, the best structural and environmental performances can be achieved not only by replacing cement with mineral admixtures, but also adding steel fibers.
On 18 January 2017 a catastrophic avalanche destroyed the Rigopiano Gran Sasso Resort & Wellness (Rigopiano Hotel) in the Gran Sasso National Park in Italy, with 40 people trapped and a death toll of ...29. This article describes the location of the disaster and the general meteorological scenario, with field investigations to provide insight on the avalanche dynamics and its interaction with the hotel buildings. The data gathered in situ suggest that the avalanche was a fluidized dry snow avalanche, which entrained a sligthtly warmer snow cover along the path and reached extremely long runout distances with braking effect from mountain forests. The avalanche that reached the Rigopiano area was a “wood-snow” avalanche—a mixture of snow and uprooted and crushed trees, rocks, and other debris. There were no direct eyewitnesses at the event, and a quick post-event survey used a numerical model to analyze the dynamics of the event to estimate the pressure, velocity, and direction of the natural flow and the causes for the destruction of the hotel. Considering the magnitude and the damage caused by the event, the avalanche was at a high to very high intensity scale.
Many empirical equations for predicting the modulus of elasticity as a function of compressive strength can be found in the current literature. They are obtained from experiments performed on a ...restricted number of concrete specimens subjected to uniaxial compression. Thus, the existing equations cannot cover the entire experimental data. This is due to the fact that mechanical properties of concrete are highly dependent on the types and proportions of binders and aggregates. To introduce a new reliable formula, more than 3000 data sets, obtained by many investigators using various materials, have been collected and analyzed statistically. The compressive strengths of the considered concretes range from 40 to 160 MPa (5.8 to 23.2 ksi). As a result, a practical and universal equation, which also takes into consideration the types of coarse aggregates and mineral admixtures, is proposed. PUBLICATION ABSTRACT
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