This paper analyzed the quality of the xBD image-training dataset for identifying building damage across a variety of natural hazards using deep learning convolutional neural networks. Specifically, ...we evaluated the pros and cons of combining training datasets across multiple natural hazards and provided recommendations on using the provided training dataset to optimize classification accuracy for building damage detection. The xBD dataset was rebalanced, using random over-sampling and under-sampling methods. Random over-sampling randomly duplicates the minority class, while random under-sampling randomly cuts-off the majority class. With the balanced dataset, we used the xBD baseline architecture as a starting point in the classification and find that it overfit to the no damage class; therefore, we improved the base classification algorithm by modifying the top layers of ResNet50. We found that not all classes (destroyed, major damage, minor damage, and no damage) were uniformly identifiable across natural hazards; therefore, we retrained the weights from ImageNet, adding five new convolution, batch normalization, and max pooling layers on top of ResNet50. One dropout layer, with a rate of 0.5 was also added in-between the fully connected layers to reduce overfitting and improve performance. We also evaluate the identifiability of the four damage classes in the xbd dataset. Because classification performance was significantly higher for the “no damage” class as compared to “minor”, “major”, and “destroyed” classes, we evaluated merging classes. We kept the “no damage” class and created a second merged class (“damaged”) representing “minor damage,” “major damage,” and “destroyed.” We used the same architecture for the multiclass classification and the binary classification but without the ImageNet weights. Based on this work, we recommend that users be aware of performance differences across natural hazards and across damage classes. Earthquake building damage is extremely limited in the training data and, as a result, application of the trained algorithm on earthquake data cannot be evaluated given the xBD dataset. Building damage due to volcano and tsunami are also poorly represented in the training data, and do not have sufficient data for model validation (especially within all damage classes). Wind hazards are well-represented and therefore application of the algorithm trained using either the wind-only data or the multi-hazard dataset is reliable. The multi-class algorithm trained with wind hazard specific data slightly outperforms a multihazard trained multiclass model (F1 score 0.70 vs. 0.67). Both models have similar performance across all four classes (F1 > 0.5). For flood, fire, and tsunami hazards, we recommend using the binary damage classes as identifiability is low for at least two of the classes in each hazard. For flood building damage, binary classification performance resulted in a significantly higher F1 score when trained with the flood specific dataset versus the multihazard data (0.72 vs. 0.54). On the other hand, for fire building damage, classification performance is slightly higher when the model is trained on multi-hazard data, rather than trained using a fire specific dataset (F1 score 0.46 vs. 0.42).
The DNA damage response (DDR) generates transient repair compartments to concentrate repair proteins and activate signaling factors. The physicochemical properties of these spatially confined ...compartments and their function remain poorly understood. Here, we establish, based on live cell microscopy and CRISPR/Cas9‐mediated endogenous protein tagging, that 53BP1‐marked repair compartments are dynamic, show droplet‐like behavior, and undergo frequent fusion and fission events. 53BP1 assembly, but not the upstream accumulation of γH2AX and MDC1, is highly sensitive to changes in osmotic pressure, temperature, salt concentration and to disruption of hydrophobic interactions. Phase separation of 53BP1 is substantiated by optoDroplet experiments, which further allowed dissection of the 53BP1 sequence elements that cooperate for light‐induced clustering. Moreover, we found the tumor suppressor protein p53 to be enriched within 53BP1 optoDroplets, and conditions that disrupt 53BP1 phase separation impair 53BP1‐dependent induction of p53 and diminish p53 target gene expression. We thus suggest that 53BP1 phase separation integrates localized DNA damage recognition and repair factor assembly with global p53‐dependent gene activation and cell fate decisions.
Synopsis
The DNA damage response effector 53BP1 forms characteristic foci at DNA breaks, which are here found to exhibit liquid droplet‐like self‐compartmentalization important for downstream activation of the p53 tumor suppressor.
DNA repair compartments show liquid‐like properties with droplet fusions and fissions.
Phase separation behavior is conferred by 53BP1, and uncoupled from upstream DNA damage detection and γH2AX‐MDC1 accumulation.
53BP1 assembly at break sites is abolished by changes in osmotic pressure, temperature, salt concentration, or disruption of hydrophobic interactions.
53BP1 optoDroplet experiments indicate multivalency and reveal sequence elements required for clustering.
Disruption of 53BP1 phase separation impairs p53 activation and p21 induction upon DNA damage.
Self‐compartmentalization of the DNA double‐strand break response effector 53BP1 plays a key role in damage‐induced activation of p53 and its downstream targets.
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.
Das Hochwasserereignis vom Juli 2021 hat besonders in den betroffenen Gebieten von Rheinland‐Pfalz und Nordrhein‐Westfalen außerordentlich schwere strukturelle Schäden an der allgemeinen Bebauung und ...der Infrastruktur hinterlassen. Der Beitrag gibt einen Überblick über die Ergebnisse einer Schadensdokumentation, die unmittelbar nach dem Hochwasser im Ahrtal und Bad Münstereifel durchgeführt wurde. Diese setzt eine Linie von Hochwasserschadensanalysen fort, die in den letzten 20 Jahren vom Erdbebenzentrum der Bauhaus‐Universität Weimar durchgeführt wurden und in der Entwicklung eines Hochwasserschadensmodells resultierten, mit welchem derartig schwere strukturelle Schäden prognostiziert werden können. Mit der Schadensdokumentation ließen sich die vom EDAC gewonnenen Erkenntnisse über das Verhalten der verschiedenen Bauweisen unter dem Einfluss einer extremen Hochwassereinwirkung vertiefen und plausibilisieren. Die vorgefundenen Schadensbilder des Hochwassers bestätigen die Ansätze des EDAC‐Hochwasserschadensmodells und zeigen die Notwendigkeit für eine Erweiterung auf, um die Besonderheiten der Überflutungsbedingungen und des Bauwerksbestands zu berücksichtigen. Es wird die Verbindung zu weiteren Auswertungen hergestellt, indem drohnenbasierte Techniken zur schnellen Zustandserfassung der von einer Naturkatastrophe betroffenen Gebiete und des Bauwerksbestands mit dem Ziel der Generierung von realitätsgetreuen und leicht interpretierbaren Lagebildern vorgestellt werden.
The 2021 flood: engineering analysis of building damage
The flood event of July 2021 left extremely severe structural damage to the general buildings and infrastructure especially in the affected areas of Rhineland‐Palatinate and North Rhine‐Westphalia. The article gives an overview of the results of a damage documentation that was carried out immediately after the flood in the Ahr valley and Bad Münstereifel. The survey continues a line of flood damage analyses carried out over the past 20 years by the Earthquake Damage Analysis Center at the Bauhaus‐Universität Weimar, which has resulted in the development of an engineering‐based flood damage model that can takes into account such severe structural damage. With the documentation of the damage, the knowledge gained by EDAC about the behavior of the various building types under the influence of extreme flooding conditions could be deepened and checked for plausibility. The flood damage patterns found confirm the approaches of the EDAC flood damage model and show the need for an extension to take into account the special features of the building stock and the flooding conditions that occurred. A connection is given to ongoing studies, in which drone‐based techniques for rapid assessment of the status of areas affected by a natural disaster and the existing buildings are presented with the aim of generating realistic and easily interpretable situation images.
The present study details the methodology of the identification process of uncoupled damage formulations proposed by Bai and Wierzbicki (B&W model a Bai and Wierzbicki (2008) and Modified ...MohraCoulomb a Bai and Wierzbicki (2010)) as well as the Lemaitre and enhanced Lemaitre coupled damage models. These uncoupled models were first implemented in the Finite Element (FE) software Forge2009ARG, then their parameters identifications were carried out. These identifications involve two steps: identification of hardening law parameters and identification of damage parameters. In the first step, the method to obtain the coefficient of a non-linear friction law in compression test is also presented. The second step differs between the above-mentioned models: the identification of uncoupled models is carried out through the experimental fracture strains of different loading paths, while the identification of Lemaitreas model is based on the softening effect of damage. The latter model is enhanced by accounting for the influence of the Lode parameter to improve its ability to predict fracture in shear loading. The results show that, among the studied models, the proposed enhanced Lemaitre model gives overall best results in terms of fracture prediction for all the tests. The proportionality of studied loading paths is also discussed. It is shown that the compression is not suitable to identify the parameters of the studied uncoupled damage models.
Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous ...damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.
Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more ...frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-flood-prone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.
Low-velocity impact damage can drastically reduce the residual strength of a composite structure even when the damage is barely visible. The ability to computationally predict the extent of damage ...and compression-after-impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant time and cost penalties. A high-fidelity three-dimensional composite damage model, to predict both low-velocity impact damage and CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. The intralaminar damage model component accounts for physically-based tensile and compressive failure mechanisms, of the fibres and matrix, when subjected to a three-dimensional stress state. Cohesive behaviour was employed to model the interlaminar failure between plies with a bi-linear traction–separation law for capturing damage onset and subsequent damage evolution. The virtual tests, set up in ABAQUS/Explicit, were executed in three steps, one to capture the impact damage, the second to stabilize the specimen by imposing new boundary conditions required for compression testing, and the third to predict the CAI strength. The observed intralaminar damage features, delamination damage area as well as residual strength are discussed. It is shown that the predicted results for impact damage and CAI strength correlated well with experimental testing without the need of model calibration which is often required with other damage models.
•We present an approach for obtaining damage variables for Plastic Damage Models.•Approach is suitable for describing monotonic behavior of RC structures.•Advantages: mesh-insensitive, continuum ...mechanics-based, no calibration required.•A particular algorithm is presented and implemented in Abaqus.
The behavior of reinforced concrete (RC) structures under severe demands, as strong ground motions, is highly complex; this is mainly due to joint operation of concrete and steel, with several coupled failure modes. Furthermore, given the increasing awareness and concern for the important seismic worldwide risk, new developments have arisen in earthquake engineering. Nonetheless, simplified numerical models are widely used (given their moderate computational cost), and many developments rely mainly on them. The authors have started a long-term research whose final objective is to provide, by using advanced numerical models, solid basis for these developments. Those models are based on continuum mechanics, and consider Plastic Damage Model to simulate concrete behavior. Within this context, this paper presents a new methodology to calculate damage variables evolution; the proposed approach is based in the Lubliner/Lee/Fenves formulation and provides closed-form expressions of the compressive and tensile damage variables in terms of the corresponding strains. This methodology does not require calibration with experimental results and incorporates a strategy to avoid mesh-sensitivity. A particular algorithm, suitable for implementation in Abaqus, is described. Mesh-insensitivity is validated in a simple tension example. Accuracy and reliability are verified by simulating a cyclic experiment on a plain concrete specimen. Two laboratory experiments consisting in pushing until failure two 2-D RC frames are simulated with the proposed approach to investigate its ability to reproduce actual monotonic behavior of RC structures; the obtained results are also compared with the aforementioned simplified models that are commonly employed in earthquake engineering.
Neurons are highly susceptible to DNA damage accumulation due to their large energy requirements, elevated transcriptional activity, and long lifespan. While newer research has shown that DNA breaks ...and mutations may facilitate neuron diversity during development and neuronal function throughout life, a wealth of evidence indicates deficient DNA damage repair underlies many neurological disorders, especially age‐associated neurodegenerative diseases. Recently, efforts to clarify the molecular link between DNA damage and neurodegeneration have improved our understanding of how the genomic location of DNA damage and defunct repair proteins impact neuron health. Additionally, work establishing a role for senescence in the aging and diseased brain reveals DNA damage may play a central role in neuroinflammation associated with neurodegenerative disease.
Deficient DNA damage repair underlies neurological disorders and age‐associated neurodegenerative diseases. This review discusses our current understanding of how the genomic location of DNA damage and defunctional repair proteins impact neuron health.
PDF
