A preliminary analysis and discussion of the condominium building collapse in surfside, Florida, US, June 24, 2021

On June 24, 2021, a 40-year-old reinforced concrete flat plate structure building in Miami suffered a sudden partial collapse. This study analyzed the overall performance and key components of the collapsed building based on the building design codes (ACI-318 and GB 50010). Punching shear and post-punching performances of typical slab-column joints are also studied through the refined finite element analysis. The collapse process was simulated and visualized using a physics engine. By way of these analyses, weak design points of the collapsed building are highlighted. The differences between the reinforcement detailing of the collapsed building and the requirements of the current Chinese code are discussed, together with a comparison of the punching shear and post-punching performances. The simulated collapse procedure and debris distribution are compared with the actual collapse scenes.

A Methodological Approach for Assessing the Safety of Historic Buildings’ Façades

Inefficiency in maintaining and managing architectural heritage threatens both heritage conservation and public safety. Damage related to collapsed building elements requires an investigation into the factors which cause these phenomena in order to prevent them and to mitigate their effects. This paper aims to define a methodological approach for assessing the risk to humans of falling bodies from historic buildings’ façades. The method is based on the identification of a group of parameters to assess façade’s hazards, vulnerability and public exposure. The results provide the identification of risk factors and related affecting parameters, proposing a synthetic indicator to quantify the risk. The proposal is original in the field of both maintenance planning and preventive maintenance, intending to preserve architectural heritage and public safety. The results lead to an easy tool, as a map, to prioritise risk mitigation interventions. Such a tool, if integrated into maintenance tenders, allows the evaluation, in the context of condition-based maintenance, of the need for interventions.

Collapsed Building Detection Using 3D Point Clouds and Deep Learning

Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as depth information cannot be obtained, it is difficult to detect collapsed buildings when their roofs are not heavily damaged. Airborne LiDAR can efficiently obtain the 3D geometries of buildings (in the form of point clouds) and thus has greater potential to detect various collapsed buildings. However, there have been few previous studies on deep learning-based damage detection using point cloud data, due to a lack of large-scale datasets. Therefore, in this paper, we aim to develop a dataset tailored to point cloud-based building damage detection, in order to investigate the potential of point cloud data in collapsed building detection. Two types of building data are created: building roof and building patch, which contains the building and its surroundings. Comprehensive experiments are conducted under various data availability scenarios (pre–post-building patch, post-building roof, and post-building patch) with varying reference data. The pre–post scenario tries to detect damage using pre-event and post-event data, whereas post-building patch and roof only use post-event data. Damage detection is implemented using both basic and modern 3D point cloud-based deep learning algorithms. To adapt a single-input network, which can only accept one building’s data for a prediction, to the pre–post (double-input) scenario, a general extension framework is proposed. Moreover, a simple visual explanation method is proposed, in order to conduct sensitivity analyses for validating the reliability of model decisions under the post-only scenario. Finally, the generalization ability of the proposed approach is tested using buildings with different architectural styles acquired by a distinct sensor. The results show that point cloud-based methods can achieve high accuracy and are robust under training data reduction. The sensitivity analysis reveals that the trained models are able to locate roof deformations precisely, but have difficulty recognizing global damage, such as that relating to the roof inclination. Additionally, it is revealed that the model decisions are overly dependent on debris-like objects when surroundings information is available, which leads to misclassifications. By training on the developed dataset, the model can achieve moderate accuracy on another dataset with different architectural styles without additional training.

Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt

Buildings are vulnerable to collapse incidents. We adopt a workflow to detect unusual vertical surface motions before building collapses based on PS-InSAR time series analysis and spatiotemporal data mining techniques. Sentinel-1 ascending and descending data are integrated to decompose vertical deformation in the city of Alexandria, Egypt. Collapsed building data were collected from official sources, and overlayed on PS-InSAR vertical deformation results. Time series deformation residuals are used to create a space–time cube in the ArcGIS software environment and analyzed by emerging hot spot analysis to extract spatiotemporal patterns for vertical deformation around collapsed buildings. Our results show two spatiotemporal patterns of new cold spot or new hot spot before the incidents in 66 out of 68 collapsed buildings between May 2015 and December 2018. The method was validated in detail on four collapsed buildings between January and May 2019, proving the applicability of this workflow to create a temporal vulnerability map for building collapse monitoring. This study is a step forward to create a PS-InSAR based model for building collapse prediction in the city.

Síndrome de aplastamiento

Compartment Syndrome is defined as the elevation of tissue pressure above 30 mmHg in a compartment and because comparing the compartment tissue pressure with the diastolic blood pressure below 30 mmHg has proved to be more reliable, this is now a predecessor of Crush Syndrome, which is defined as a post traumatic rhabdomyolysis with systemic distress mainly associated with acute renal failure. The use of amputation as a method to improve the patient’s clinical condition is still controversial, thereby we present the clinical case of a patient rescued from a collapsed building 24 hours after the earthquake that affected Mexico City on September 19, 2017, followed by an updated literature review. Key words: Compartimental; crush syndrome; earthquake; amputation; trauma.

SPOTTER: Detection of Human Beings Under Collapsed Environment

Collapse of man-made structures, such as buildings and bridges earth quakes and fire accident, occur with varying frequency across the world. In such a scenario, the survived human beings are likely to get trapped in the cavities created by collapsed building material. During post disaster rescue operations, searchand-rescue crews have a very limited or no knowledge of the presence, location, and number of the trapped victims. Deep learning is a fast-growing domain of machine learning, mainly for solving problems in computer vision. One of the implementation of deep learning is detection of objects including humans, based on video stream. Thus, the presence of a human buried under earthquake rubble or hidden behind barriers can be identified using deep learning. This is done with the help of USB camera which can be inserted into the rubble. Spotter also gives an audio message about the location of the human presence and gives the area where the human is likely to be present. Human detection is done with the help of Computer Vision using OpenCV.

COLLAPSED BUILDING CLASSIFICATION WITH OPTICAL AND SAR DATA BASED ON MANIFOLD LEARNING

The collapse of buildings is a major factor in the casualties and economic losses of earthquake disasters, and the degree of building collapse is an important indicator for disaster assessment. In order to improve the classification of collapsed building coverings (CBC), a new fusion technique was proposed to integrate optical data and SAR data at the pixel level based on manifold learning.Three typical manifold learning models, namely, Isometric Mapping(ISOMAP), Local Linear Embedding (LLE) and principle component analysis (PCA), were used, and their results were compared. Feature extraction were employed from SPOT-5 data with RADARSAT-2 data. Experimental results showed that 1) the most useful features of the optical and SAR data were contained in manifolds with low-intrinsic dimensionality, while various CBC classes were distributed differently throughout the low- dimensionality spaces of manifolds derived from different manifold learning models; 2) in some cases, the performance of Isomap is similar to PCA, but PCA generally performed the best in this study, yielding the best accuracy of all CBC classes and requiring the least amount of time to extract features and establish learning; and 3) the LLE-derived manifolds yielded the lowest accuracy, mainly by confusing soil with collapsed building and rock. These results show that the manifold learning can improve the effectiveness of CBC classification by fusing the optical and SAR data features at the pixel level, which can be applied in practice to support the accurate analysis of earthquake damage.