scholarly journals A new method for earthquake-induced damage identification in historic masonry towers combining OMA and IDA

2021 ◽  
Author(s):  
Alban Kita ◽  
Nicola Cavalagli ◽  
Ilaria Venanzi ◽  
Filippo Ubertini
Keyword(s):  
Damage Identification ◽  
Central Italy ◽  
Surrogate Models ◽  
Moderate Intensity ◽  
Damage Localization ◽  
Historic Masonry ◽  
Bell Tower ◽  
Masonry Towers ◽  
Main Shocks ◽  
Non Linear

AbstractThis paper presents a novel method for rapidly addressing the earthquake-induced damage identification task in historic masonry towers. The proposed method, termed DORI, combines operational modal analysis (OMA), FE modeling, rapid surrogate modeling (SM) and non-linear Incremental dynamic analysis (IDA). While OMA-based Structural Health Monitoring methods using statistical pattern recognition are known to allow the detection of small structural damages due to earthquakes, even far-field ones of moderate intensity, the combination of SM and IDA-based methods for damage localization and quantification is here proposed. The monumental bell tower of the Basilica of San Pietro located in Perugia, Italy, is considered for the validation of the method. While being continuously monitored since 2014, the bell tower experienced the main shocks of the 2016 Central Italy seismic sequence and the on-site vibration-based monitoring system detected changes in global dynamic behavior after the earthquakes. In the paper, experimental vibration data (continuous and seismic records), FE models and surrogate models of the structure are used for post-earthquake damage localization and quantification exploiting an ideal subdivision of the structure into meaningful macroelements. Results of linear and non-linear numerical modeling (SM and IDA, respectively) are successfully combined to this aim and the continuous exchange of information between the physical reality (monitoring data) and the virtual models (FE models and surrogate models) effectively enforces the Digital Twin paradigm. The earthquake-induced damage identified by both data-driven and model-based strategies is finally confirmed by in-situ visual inspections.

Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

2021 ◽  
pp. 147592172110339
Author(s):  
Guoqiang Liu ◽  
Binwen Wang ◽  
Li Wang ◽  
Yu Yang ◽  
Xiaguang Wang
Keyword(s):  
Distribution Function ◽  
Diagnostic Imaging ◽  
Weight Distribution ◽  
Composite Structures ◽  
Damage Identification ◽  
Guided Wave ◽  
Composite Panel ◽  
Damage Localization ◽  
Localization Accuracy ◽  
Direct Interpretation

Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.

scholarly journals Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 180
Author(s):  
Lei Fu ◽  
Qizhi Tang ◽  
Peng Gao ◽  
Jingzhou Xin ◽  
Jianting Zhou
Keyword(s):  
Short Term Memory ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Damage Localization ◽  
Short Term ◽  
Term Memory ◽  
Localization Accuracy ◽  
Long Span Bridges ◽  
Long Span ◽  
Long Short Term Memory

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

Damage Identification for Simply-Supported Bridge Based on SVM Optimized by PSO (PSO-SVM)

2011 ◽  
Vol 80-81 ◽  
pp. 490-494 ◽  
Author(s):  
Han Bing Liu ◽  
Yu Bo Jiao ◽  
Ya Feng Gong ◽  
Hai Peng Bi ◽  
Yan Yi Sun
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Damage Identification ◽  
Fitness Function ◽  
Support Vector ◽  
Detection Accuracy ◽  
Damage Localization ◽  
Swarm Optimization ◽  
Simply Supported ◽  
Beam Bridge

A support vector machine (SVM) optimized by particle swarm optimization (PSO)-based damage identification method is proposed in this paper. The classification accuracy of the damage localization and the detection accuracy of severity are used as the fitness function, respectively. The best and can be obtained through velocity and position updating of PSO. A simply supported beam bridge with five girders is provided as numerical example, damage cases with single and multiple suspicious damage elements are established to verify the feasibility of the proposed method. Numerical results indicate that the SVM optimized by PSO method can effectively identify the damage locations and severity.

Seismic Performance Assessment of Existing Steel Buildings: A Case Study

2018 ◽  
Vol 763 ◽  
pp. 1067-1076 ◽  
Author(s):  
Luigi di Sarno ◽  
Fabrizio Paolacci ◽  
Anastasios G. Sextos
Keyword(s):  
Numerical Study ◽  
Central Italy ◽  
Steel Structures ◽  
Eurocode 8 ◽  
Building Structures ◽  
Steel Buildings ◽  
Seismic Performance Assessment ◽  
Masonry Infills ◽  
Main Shocks

Numerous existing steel framed buildings located in earthquake prone regions world-wide were designed without seismic provisions. Slender beam-columns, as well as non-ductile beam-to-column connections have been employed for multi-storey moment-resisting frames (MRFs) built before the 80’s. Thus, widespread damage due to brittle failure has been commonly observed in the past earthquakes for steel MRFs. A recent post-earthquake survey carried out in the aftermath of the 2016-2017 Central Italy seismic swarm has pointed out that steel structures may survive the shaking caused by several main-shocks and strong aftershocks without collapsing. Inevitably, significant lateral deformations are experienced, and, in turn, non-structural components are severely damaged thus inhibiting the use of the steel building structures. The present papers illustrates the outcomes of a recent preliminary numerical study carried out for the case of a steel MRF building located in Amatrice, Central Italy, which experienced a series of ground motion excitations suffering significant damage to the masonry infills without collapsing. A refined numerical model of the sample structure has been developed on the basis of the data collected on site. Given the lack of design drawings, the structure has been re-designed in compliance with the Italian regulations imposed at the time of construction employing the allowable stress method. The earthquake performance of the case study MRF has been then investigated through advanced nonlinear dynamic analyses and its structural performance has been evaluated according to Eurocode 8-Part 3 for existing buildings. The reliability of the codified approaches has been evaluated and possible improvements emphasized.

Spatio-temporal variation of Anaelastic and Scattering Seismic Attenuation during the 2016-2017 Central Italy Seismic Sequence

2021 ◽  
Author(s):  
Simona Gabrielli ◽  
Aybige Akinci ◽  
Ferdinando Napolitano ◽  
Luca De Siena ◽  
Edoardo Del Pezzo ◽  
...  
Keyword(s):  
Wave Attenuation ◽  
Central Italy ◽  
Temporal Variations ◽  
Seismic Attenuation ◽  
Coda Wave ◽  
Seismic Sequence ◽  
Rock Fracturing ◽  
Low Frequencies ◽  
Main Shocks ◽  
Stress Changes

<p>Between August and October 2016, the Central Apennines in Italy have been struck by a long-lasting seismic sequence, known as the Amatrice (Mw 6.0) - Visso (Mw 5.9) - Norcia (Mw 6.5) sequence. The cascading ruptures occurred in this sequence have been considered connected to the fluid migration in the fault network, as suggested by previous studies. The behaviour of fluids in the crust is crucial to understand earthquakes occurrence and stress changes since fluids reduce fault stability. It has long been understood that the seismic attenuation is strongly controlled by the structural irregularity and heterogeneities; micro-cracks and cavities, either fluid-filled or dry, temperature and pressure variations cause a decrease in seismic wave amplitude and pulse broadening. Hence seismic attenuation imagining is a powerful tool to be a relevant provenance of information about the influence and abundance of fluids in a seismic sequence.</p><p>The aim of this work is to separate scattering and absorption contributions to the total attenuation of coda waves and to provide their spatial and temporal variations at different frequency bands of these quantities using two datasets: the first one comprising 592 earthquakes occurred before the sequence (March 2013-August 2016) and the second one comprising 763 events (ML > 2.8) from the Amatrice-Visso-Norcia sequence. Scattering and absorption have been measured through peak-delay and coda-wave attenuation parameters (the latter inverted using frequency-dependent sensitivity kernels).</p><p>The preliminary results show a clear difference between the pre-sequence and sequence images, mainly at low frequencies (1.5 Hz), where we can define a spatial increase of scattering with time attributed to rock fracturing and fluid circulation. The coda attenuation tomography also demonstrates a clear variation between the pre-sequence and the sequence over series of time windows being before and after the largest main shocks of the seismic sequence, with an increase of the attenuation in space with decreasing time. The peak delay indicates a high scattering area corresponding to the Gran Sasso massif and L’Aquila zone, where an important seismic sequence (Mw 6.3) occurred in 2009.</p>

Analysis of a database of landslides triggered by the 2016 Central Italy seismic sequence  

2021 ◽  
Author(s):  
Giovanni Forte ◽  
Melania De Falco ◽  
Federica Iannicelli ◽  
Antonio Santo
Keyword(s):  
Central Italy ◽  
Google Earth ◽  
Mountain Range ◽  
Seismic Sequence ◽  
Large Area ◽  
Geometrical Characterization ◽  
Seismic Parameters ◽  
Main Shocks ◽  
Ground Effects ◽  
Transportation Routes

<p>The seismic sequence that struck Central Italy in 2016 was characterized by three main shocks respectively occurred on August 24<sup>th</sup> Mw 6.0; October 26<sup>th</sup> Mw5.9 and October 30<sup>th</sup> Mw 6.5. The seismic sequence caused several ground effects over a large area of ​​the central Apennine mountain range, mainly affecting transportation routes.</p><p>In the aftermath of the sequence, several research groups mapped around 820 landslides involving road cuts in rock and fill slopes over an area of about 2000 km<sup>2</sup> (GEER,ISPRA, C.E.R.I. by Roma La Sapienza). These data are summarized in the CEDIT catalog by Martino et al., (2017), where almost 150, 250 and 420 instability phenomena were respectively triggered by each mainshock. Further updates were carried out by the Authors in the framework of the Reluis projects of the Department of Civil Protection. In particular, other 550 phenomena were mapped by interpretation of aero photos provided by google-earth. For some of the largest ones, field surveys were carried out for mechanical, structural, and geometrical characterization.</p><p>The dataset distribution was analyzed with geological, geomorphological, and seismic parameters, such as lithology, fault distance, landslide run-out, estimates of mobilized volumes, distance from the epicenter, PGA, and damages.</p><p>The triggered events are mainly characterized by Category I of Keefer (1984) classification, namely rockfalls and rockslides. The maximum triggering distance resulted as high as 50 km far from the epicenter. The most affected areas are characterized by ridge crests or flanks of valleys in carbonate rocks.</p><p>This study permitted to highlight the most relevant parameters for the assessment of earthquake-triggered susceptibility for the study area and identify some meaningful and critical case studies for the future development of the research.</p><p> </p>

Active Faulting in Source Region of 2016–2017 Central Italy Event Sequence

2018 ◽  
Vol 34 (4) ◽  
pp. 1557-1583 ◽  
Author(s):  
Fabrizio Galadini ◽  
Emanuela Falcucci ◽  
Stefano Gori ◽  
Paolo Zimmaro ◽  
Daniele Cheloni ◽  
...  
Keyword(s):  
Source Region ◽  
Central Italy ◽  
Active Faults ◽  
Earthquake Sequence ◽  
Fault System ◽  
Fault Plane Solutions ◽  
Moment Tensors ◽  
Central Italy Earthquake ◽  
Finite Fault ◽  
Main Shocks

The Central Italy earthquake sequence produced three main shocks: M6.1 24 August, M5.9 26 October, and M6.5 30 October 2016. Additional M5–5.5 events struck this territory on 18 January 2017 in the Campotosto area. Fault plane solutions for the main shocks exhibit normal faulting (characteristic of crustal extension occurring in the inner central Apennines). Significant evidence, including hypocenter locations, strike and dip angles of the moment tensors, inverted finite fault models (using GPS, interferometric aperture radar, and ground motion data), and surface rupture patterns, all point to the earthquakes having been generated on the Mt. Vettore–Mt. Bove fault system (all three main shocks) and on the Amatrice fault, in the northern sector of the Laga Mountains (portion of 24 August event). The earthquake sequence provides examples of both synthetic and antithetic ruptures on a single fault system (30 October event) and rupture between two faults (24 August event). We describe active faults in the region and their segmentation and present understanding of the potential for linkages between segments (or faults) in the generation of large earthquakes.

Sign in / Sign up

Export Citation Format

Share Document