A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.

Statistical analysis and modeling to examine the exterior and interior building damage pertaining to the 2016 Kumamoto earthquake

After an earthquake occurs, field surveys are conducted by relevant authorities to assess the damage suffered by buildings. The field survey is essential as it ensures the safety of residents and provides the necessary information to local authorities for post-disaster recovery. In Japan, a primary (mandatory) exterior survey is conducted first, and a secondary (voluntary) interior survey is performed subsequently if the residents request a reinvestigation. However, a major challenge associated with field surveys is the substantial time cost of determining the damage grades. Moreover, an interior survey is performed only after receiving the reinvestigation request from occupants, which further delays the decision-making process. In addition, the risk of incorrect damage estimation during the exterior survey must be considered because underestimating the damage can endanger the residents. Therefore, in this study, a three-part analysis (Parts I–III), where each part corresponds to a distinct stage of the standard damage assessment procedure, was performed to characterize the relationship between the building parameters and damage grades at different stages. To further explore the possibility of accelerating decision-making, predictive modeling was performed in each part. The Part I results indicate that estimating the final damage grade for all buildings immediately after the exterior survey is similar to treating the exterior survey results as the final ones. The Part II results show that buildings that potentially require an interior survey can be predicted with reasonable accuracy after the exterior survey. In buildings for which reinvestigations have been requested, Part III demonstrates that the risk of underestimation in the exterior survey can be predicted reliably.

Transplantation of mesenchymal stem cell sheet to treat acute hind-limb ischemia: A preclinical study

Introduction: Mesenchymal stem cell (MSC) transplantation has been reported as a promising therapy for acute limb ischemia (ALI). However, the treatment efficacy is limited to only certain improvements. Therefore, this study aims to improve the treatment efficacy of MSC transplantation through the use of MSC sheets produced from MSCs' cultured in fibrin scaffold (Fi-MSCs) in ALI models. Methods: MSCs were isolated and expanded from human umbilical cord tissue. The fibril scaffold was produced from human umbilical cord blood. Fi-MSCs were prepared by mixing MSCs with fibril according to a published protocol, and the Fi-MSC sheets were implanted directly into ligated and transected sites in the hind limbs of ischemic models (treatment group — group I). The results were compared with that of the control group (group II) in which mice were injected with saline. The treatment efficacy was recorded and evaluated through the following assays: limb morphology, SpO2, blood perfusion, angiogenesis, and histological morphology on days 7, 14, and 28 after treatment. Results: The results indicate that the transplantation of Fi-MSC sheets positively affected the acute ischemia hind-limb mouse models. On day 7 post-transplantation, the SpO2 index recorded at feet in group I (treatment) significantly increased from 79.24% + 1.43% to 89.40% + 1.65% (p-value < 0.05), while in group II (control), the SpO2 index slightly increased from 76.52% + 1.63% to 77.00 + 1.15 (p-value > 0.05). Besides, there were 60.00% (auto-recovery), 13.33%, and 26.67% mice at damage grades 0, I, and II, respectively, in the control group compared to 80%, 20%, and 0% mice at damage grades 0, I, and II, respectively. Moreover, in group I, all mice showed improved blood reperfusion, neovascular, and repaired muscle tissue compared to group II. Conclusion: Fi-MSC sheet transplantation positively reduced injury and improved blood perfusion ALI in the Swiss mouse model.

Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution

In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the conjugate Dirichlet distribution of multinomial distribution is selected as a prior distribution, and Dempster–Shafer evidence theory (D-S theory) is introduced to fuse multisource previous information. Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. The study result shows that the Bayesian inference method based on multinomial distribution is highly maneuverable and can be used to predict ammunition demand of different damage grades under the condition of small samples.

Classification of earthquake-induced building damage using innovative methods

<p>In the event of an earthquake, damaged and destroyed buildings are of central importance. Using a combination of automatic approaches and human crowdsourced visual interpretation based on unmanned aerial vehicle (UAV) derived data for the classification of earthquake damage offers a fast and objective assessment of the damage situation. Earthquake engineering knowledge is transferred to these innovative methods by developing and implementing a damage catalogue. This damage catalogue includes typical damage patterns for five damage grades ranging from crack widths to failure modes and focuses on the two common building materials - reinforced concrete and masonry. This paper presents the structure of such damage catalogue, defines crack widths and gives examples for particular damage grades. Moreover, the application of the damage catalogue in automatic and crowdsourcing approaches for a classification into five damage grades is explained.</p>

A generic physical vulnerability model for floods: review and concept for data-scarce regions

The use of different methods for physical flood vulnerability assessment has evolved over time, from traditional single-parameter stage–damage curves to multi-parameter approaches such as multivariate or indicator-based models. However, despite the extensive implementation of these models in flood risk assessment globally, a considerable gap remains in their applicability to data-scarce regions. Considering that these regions are mostly areas with a limited capacity to cope with disasters, there is an essential need for assessing the physical vulnerability of the built environment and contributing to an improvement of flood risk reduction. To close this gap, we propose linking approaches with reduced data requirements, such as vulnerability indicators (integrating major damage drivers) and damage grades (integrating frequently observed damage patterns). First, we present a review of current studies of physical vulnerability indicators and flood damage models comprised of stage–damage curves and the multivariate methods that have been applied to predict damage grades. Second, we propose a new conceptual framework for assessing the physical vulnerability of buildings exposed to flood hazards that has been specifically tailored for use in data-scarce regions. This framework is operationalized in three steps: (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking resulting index classes with damage patterns, utilizing a synthetic “what-if” analysis. The new framework is a first step for enhancing flood damage prediction to support risk reduction in data-scarce regions. It addresses selected gaps in the literature by extending the application of the vulnerability index for damage grade prediction through the use of a synthetic multi-parameter approach. The framework can be adapted to different data-scarce regions and allows for integrating possible modifications to damage drivers and damage grades.

Empirical damage and liquefaction fragility curves from 2012 Emilia earthquake data

The study focuses on the effects of liquefaction on structures taken from data on about 1000 private residential masonry buildings located in several municipalities struck by the 2012 Emilia earthquake. Survey data were collected by teams of experts coordinated by the Italian Department of Civil Protection in the immediate post-earthquake emergency phase. They included information on building characteristics and the level and extent of the damage to structural and non-structural components. Furthermore, according to data related to the reconstruction process, information on the liquefaction-induced type and extent of the damage was also collected. Through a comparative analysis of the empirical damage, it was found that liquefaction strongly affected the buildings, confirming its relevance in the damage scenario under specific subsoil conditions. Based on this evidence, the article proposes a correlation between structural damage and liquefaction when it comes to deriving proper preliminary empirical fragility curves. A suitable parameter to define liquefaction effects at ground level is introduced and correlated to damage grades defined according to the European Macroseismic Scale: EMS-98.

An adaptive regional vulnerability assessment model: Review and concepts for data-scarce regions

Although the vulnerability indicator method has been applied to several data-scarce regions, a missing linkage with damage grades had hindered its application for loss evaluation to complement disaster risk reduction efforts. To address this gap, we present a review of physical vulnerability indicators and flood damage models to gain insights on best practice. Thereafter, we present a conceptual framework for linking the vulnerability indicators and damage grades using three phases (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking vulnerability index classes with damage grades. The vulnerability index comprehensively integrates elements of the hazard using a Building Impact Index (BII) on one hand, and exposure, susceptibility and local protection elements using a Building Resistance Index (BRI) on the other hand. For the damage grades, local expert assessments are used for identifying and categorizing frequently observed regional damage patterns. Finally, by means of synthetic what-if analysis, experts are asked to estimate damage grades for each interval of the BII and class of BRI to develop a vulnerability curve. The proposed conceptual framework can be used for damage prediction in data-scarce regions to support loss assessment and to provide guidance for disaster risk reduction.

The Relationship Between Proteoglycan Loss, Overloading-Induced Collagen Damage, and Cyclic Loading in Articular Cartilage

Objective The interaction between proteoglycan loss and collagen damage in articular cartilage and the effect of mechanical loading on this interaction remain unknown. The aim of this study was to answer the following questions: (1) Is proteoglycan loss dependent on the amount of collagen damage and does it depend on whether this collagen damage is superficial or internal? (2) Does repeated loading further increase the already enhanced proteoglycan loss in cartilage with collagen damage? Design Fifty-six bovine osteochondral plugs were equilibrated in phosphate-buffered saline for 24 hours, mechanically tested in compression for 8 hours, and kept in phosphate-buffered saline for another 48 hours. The mechanical tests included an overloading step to induce collagen damage, creep steps to determine tissue stiffness, and cyclic loading to induce convection. Proteoglycan release was measured before and after mechanical loading, as well as 48 hours post-loading. Collagen damage was scored histologically. Results Histology revealed different collagen damage grades after the application of mechanical overloading. After 48 hours in phosphate-buffered saline postloading, proteoglycan loss increased linearly with the amount of total collagen damage and was dependent on the presence but not the amount of internal collagen damage. In samples without collagen damage, repeated loading also resulted in increased proteoglycan loss. However, repeated loading did not further enhance the proteoglycan loss induced by damaged collagen. Conclusion Proteoglycan loss is enhanced by collagen damage and it depends on the presence of internal collagen damage. Cyclic loading stimulates proteoglycan loss in healthy cartilage but does not lead to additional loss in cartilage with damaged collagen.

Damage Assessment of Prestressed Concrete Girders Using Crack Fractal and Frequency

<p>Two novel approaches are proposed in the present study to assess the damage degree of girders based on the fractal analysis of crack patterns and natural frequency. Two full scale simply‐supported prestressed concrete box girders were firstly designed and subjected to three‐point repeated load bending tests. Concrete cracking, deformation and natural frequency of the girder were investigated during the test. Then, the box‐counting method was improved by rectangle box to analyze the fractal dimension (FD) of girders based on the flexural crack pattern in each loading stage. It shows that the surface crack patterns of the tested girders possessed definite fractal character. The FD of the girders has also an approximately bilinear relationship with the natural frequency of girders. The turning point is very close to the yielding of the prestressed tendons in the girders. Based on this, a damage index is proposed to estimate the remnant stiffness of the girders based on the FD of visible cracks. The FD frequency curves can also be employed to discriminate the damage grades of the girders. Both of the two approaches have been verified with high accuracy.</p>