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Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

Recent advancements in deep learning have found compelling applications in the image-based inspection of infrastructure systems. However, the efficacy of these models hinges significantly on the quality and diversity of the input data used for training. In ...

This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation tec ...

Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent development ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

We present a pipeline for the automatic generation of digital twins of free-standing buildings for damage assessment. Our method takes multiple view images of the target building as input and processes them to produce a 3D model in levels of detail (LOD) f ...

An image-based methodology is presented for the automatic generation of geometric building models at LOD4, incorporating both interior and exterior geometrical information. Existing approaches often focus on simplified geometries for either exteriors or in ...

Despite the variability of urban infrastructure, unreinforced masonry buildings remain globally prevalent. Constructed from brick, hollow concrete blocks, stone, or other masonry materials, these structures account for a significant proportion of fatalitie ...

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

Neural radiance fields have emerged as a dominant paradigm for creating complex 3D environments incorporating synthetic novel views. However, 3D object removal applications utilizing neural radiance fields have lagged behind in effectiveness, particularly ...

Currently, post-earthquake assessment is performed visually, which implies that inspectors visit the damaged buildings and, based on their expertise, assign a damage grade. Yet, visual inspection is subjective, time-consuming and difficult to document. In ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

Bryan German Pantoja Rosero

Chattez avec Graph Search

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Automated image-based generation of finite element models for masonry buildings

Uncertainty quantification for a deep learning models for image-based crack segmentation

Generating LOD3 building models from structure-from-motion and semantic segmentation

Damage-augmented digital twins towards the automated inspection of buildings

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins

Seismic assessment of unreinforced masonry façades from images using macroelement-based modeling

TOPO-Loss for continuity-preserving crack detection using deep learning

Automatic Removal of Non-Architectural Elements in 3D Models of Historic Buildings with Language Embedded Radiance Fields

Towards real-time digital twins for post-earthquake damage assessment of masonry buildings

An image convolution-based method for the irregular stone packing problem in masonry wall construction

Digital Twins of Stone Masonry Buildings for Damage Assessment