Damage due to age or accumulated effects from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is ...vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense three-dimensional point clouds. This paper investigates the use of high resolution three-dimensional terrestrial laser scans coupled with images to capture geometric range data of complex scenes for surface damage detection and quantification. Although using images with varying resolution to detect cracks is an extensively researched topic, damage detection using laser scanners with and without color images is a new research area that holds many opportunities for enhancing the current practice of visual inspections. Thus, this paper mainly focuses on combining the best features of laser scans and images to create an automatic and effective surface damage detection method, which will reduce the need for skilled labor during visual inspections and allow automatic documentation of related information. A novel surface normal-based damage detection and quantification method that uses the local surface properties extracted from laser scanner data along with color information is presented. Color data provides information in the fourth dimension that enables detecting damage types such as cracks, corrosion, and related surface defects that are generally difficult to identify using only laser scanner.
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•Novel surface damage detection strategy for locating and quantifying defects•Using model properties of surfaces to locate the defective areas on structures•Clustering methodology for grouping defect point into individual damage clusters•Silhouette-based cluster evaluation for optimizing the number of defect clusters•Mesh-grid based quantification system for damage area and volume determination
Recent earthquakes have shown susceptibility of unreinforced masonry (URM) buildings to damage accumulation in seismic sequences or long duration ground motions. Current structural modelling ...approaches commonly disregard the damage accumulation in URM buildings or they are unable to accurately capture this phenomenon unless sophisticated FEM models are employed. Such models are not feasible in risk‐based applications given the required large number of dynamic analyses needed to develop robust fragility and vulnerability curves. On the other hand, the common displacement‐based engineering demand parameters (EDP), such as inter‐story drift ratio, fail to capture the impact of damage accumulation in simpler, more manageable models. An alternative is to use advanced damage indices that are capable of monitoring the monotonic accumulation of damages. This study proposes a displacement‐ and energy‐based damage index that uses, as the basis, the Park and Ang damage index, modified and calibrated through experimental data of individual URM elements. Our calibration procedure maps the physical observed damage states to the hysteretic response of the elements. Consequently, damage on the individual elements is aggregated to define a global damage state at building level. Validation is carried out based on an equivalent frame model of a building tested on a shake table. Additionally, the model is subjected to multiple ground motions of seismic sequences and to long‐duration ground motions to evaluate the performance of the proposed damage index. In comparison with displacement‐based damage measures, the proposed damage index shows a superior ability to capture cumulative damage, even when simplified models are employed.
Communities around the United States face the threat of being
underwater. This is not only a matter of rising waters reaching the
doorstep. It is also the threat of being financially underwater,
...owning assets worth less than the money borrowed to obtain them.
Many areas around the country may become economically uninhabitable
before they become physically unlivable. In Underwater ,
Rebecca Elliott explores how families, communities, and governments
confront problems of loss as the climate changes. She offers the
first in-depth account of the politics and social effects of the
U.S. National Flood Insurance Program (NFIP), which provides flood
insurance protection for virtually all homes and small businesses
that require it. In doing so, the NFIP turns the risk of flooding
into an immediate economic reality, shaping who lives on the
waterfront, on what terms, and at what cost. Drawing on archival,
interview, ethnographic, and other documentary data, Elliott
follows controversies over the NFIP from its establishment in the
1960s to the present, from local backlash over flood maps to
Congressional debates over insurance reform. Though flood insurance
is often portrayed as a rational solution for managing risk, it has
ignited recurring fights over what is fair and valuable, what needs
protecting and what should be let go, who deserves assistance and
on what terms, and whose expectations of future losses are used to
govern the present. An incisive and comprehensive consideration of
the fundamental dilemmas of moral economy underlying insurance,
Underwater sheds new light on how Americans cope with loss
as the water rises.
In this paper, empirical fragility curves for reinforced concrete buildings are derived, based on post-earthquake damage data collected in the aftermath of earthquakes occurred in Italy in the period ...1976–2012. These data, made available through an online platform called Da.D.O., provide information on building position, building characteristics and damage detected on different structural components. A critical review of this huge amount of data is carried out to guarantee the consistency among all the considered databases. Then, an in-depth analysis of the degree of completeness of the survey campaign is made, aiming at the identification of the Municipalities subjected to a partial survey campaign, which are discarded from fragility analysis. At the end of this stage, only the Irpinia 1980 and L’Aquila 2009 databases are considered for further elaborations, as fully complying with these criteria. The resulting database is then integrated with non-inspected buildings sited in less affected areas (assumed undamaged), to account for the negative evidence of damage. The PGA evaluated from the shakemaps of the Italian National Institute of Geophysics and Volcanology (INGV) and a metric based on six damage levels according to EMS-98 are used for fragility analysis. The damage levels are obtained from observed damage collected during post-earthquake inspections through existing conversion rules, considering damage to vertical structures and infills/partitions. The maximum damage level observed on vertical structures and infills/partitions is then associated to the whole building. Fragility curves for two vulnerability classes, C2 and D, further subdivided into three classes of building height, are obtained from those derived for specific structural typologies (identified based on building height and type of design), using their frequency of occurrence at national level as weights.
•A framework for assessing the post-earthquake structural safety of damaged buildings is presented.•The concepts of response and damage patterns are introduced and incorporated into a systematic ...methodology integrating probabilistic seismic demand analysis, component-level damage simulation and robust assessments of the residual collapse capacity.•Machine learning algorithms are used to explicitly link the response and damage patterns to residual collapse capacity of a damaged structure, and are able to probabilistically predict the structural safety states given any available information.•A series of predictive models including Classification and Regression Trees and Random Forests are developed and examined in detail to achieve the optimal model which balance multiple performance measurements.•In contrast to previously judgement-based methods for the tagging process, this innovative approach provides a solid statistical support for structural safety assessment.•High prediction accuracies are observed based on either response and damage patterns.
A machine learning framework is presented to assess post-earthquake structural safety. The concepts of response and damage patterns are introduced and incorporated into a systematic methodology for generating a robust dataset for any damaged building. Incremental dynamic analysis using sequential ground motions is used to evaluate the residual collapse capacity of the damaged structure. Machine learning algorithms are used to map response and damage patterns to the structural safety state (safe or unsafe to occupy) of the building based on an acceptable threshold of residual collapse capacity. Predictive models including classification and regression tree and Random Forests are used to probabilistically identify the structural safety state of an earthquake-damaged building. The proposed framework is applied to a 4-story reinforced concrete special moment frame building. Distinct yet partially overlapping response and damage patterns are found for the damaged building classified as safe and unsafe. High prediction accuracies of 91% and 88% are achieved when the safety state is assessed using response and damage patterns respectively. The proposed framework could be used to rapidly evaluate whether a damaged building remains structurally safe to occupy after a seismic event and can be implemented as a subroutine in community resilience evaluation or building lifecycle performance assessment and optimization.
•A framework for real-time disaster damage monitoring and assessment.•Two main damage categories—physical and sentiment—and scale of the damage.•Quantity of disaster damage from the damage ...assessment.•Providing real-time and useful information about the damage to aid emergency response.•Assessment results are consistent with disaster type and final published results.
In the era of big data, could popularized social media platforms assist with urban damage monitoring and assessment and aid disaster rescue? Before, during, and after such disasters, citizens might disseminate disaster-related text and data through social media platforms. Therefore, social media is both a powerful and promising tool for disaster response management, including enhancing situation awareness, promoting emergency information flow, predicting disasters and coordinating rescue efforts. This study develops a framework for real-time urban disaster damage monitoring and assessment. Social media texts sent during and after the Tianjin explosion and Typhoon Nepartak (i.e., a manmade and natural large-scale disaster, respectively) disasters are collected and constitute the database. The real-time monitoring of physical damage and sentiment provides the main categories of damage and damage scale information. In this study, a physical assessment provides a detailed quantity of the losses according to the different types of damage sustained over time. One pronounced innovation is the study’s comprehensive perspective, which facilitates a thorough analysis of both the emotional and physical damage in real-time scenarios. In addition, a quantity evaluation of physical damage is performed. The findings suggest that social media can be used for rapid damage evaluations as the real-time and huge information flow contains the aforementioned damage categories, damage scale and damage quantity messages. The social media database damage assessment model presented in this study can enhance disaster situation awareness and rescue operations.
•The transition from traditional methods to ML and DL has never been discussed for vibration-based SDD.•This paper aims to fulfill this gap.•Traditional methods and comprehensive review of the modern ...ML and DL use are presented.•The review is focusing on the vibration-based structural damage detection in civil structures only.
Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Reservoir formation damage is a major problem that the oil and gas industry has to mitigate in order to maintain the oil and gas supply. A case study is presented that identifies the impacts of ...formation damage and their causes in the Nubian ‘C’ hydrocarbon reservoir within Sidki field located in the Southern Gulf of Suez, Egypt. In addition, a formation damage mitigation program was designed and implemented involving an effective stimulation treatment for each well experiencing reservoir damage. The data available for this study include core analysis to provide rock mineralogy and lithology; analysis of production fluid data; water chemistry; drilling fluid composition; perforations and well completion details; workover operations; and stimulation history. The diagnosis of formation damage based on the integrated assessment of the available data is associated with several benefits, (1) The integration of the data available helps provide a robust analysis of formation damage causes and in establishing suitable remediation actions, (2) Workover fluid is confirmed as the primary cause of reservoir damage in the studied well, (3) Several reservoir damage mechanisms were identified including water blockage, solids and filtrate invasion, fluid/rock interaction (deflocculation of kaolinite clay), salinity shock and/or high-sulfate content of the invaded fluid, (4) Irrespective of the potential causes of formation damage, the primary objective of a gas production company is to mitigate its effects and the integrated dataset helps to design appropriate and effective stimulation treatments to overcome formation damage, and (5) In gas reservoirs, especially low permeability ones, extra precautions are necessary to avoid potential reservoir damage due to workover fluid invasion.
A three-dimensional multi-fibre multi-layer micromechanical finite element model was developed for the prediction of mechanical behaviour and damage response of composite laminates. Material response ...and micro-scale damage mechanism of cross-ply, 0/90ns, and angle-ply, A-45ns, glass-fibre/epoxy laminates were captured using multi-scale modelling via computational micromechanics. The framework of the homogenization theory for periodic media was used for the analysis of the proposed amulti-fibre multi-layer representative volume elementa (M2RVE). Each layer in M2RVE was represented by a unit cube with multiple randomly distributed, but longitudinally aligned, fibres of equal diameter and with a volume fraction corresponding to that of each lamina (equal in the present case). Periodic boundary conditions were applied to all the faces of the M2RVE. The non-homogeneous stressastrain fields within the M2RVE were related to the average stresses and strains by using Gaussa theorem in conjunction with the HillaMandal strain energy equivalence principle. The global material response predicted by the M2RVE was found to be in good agreement with experimental results for both laminates. The model was used to study effect of matrix friction angle and cohesive strength of the fibreamatrix interface on the global material response. In addition, the M2RVE was also used to predict initiation and propagation of fibreamatrix interfacial decohesion and propagation at every point in the laminae.
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