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

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The increasing demand for accurate and computationally efficient modeling of light-matter interactions in stratified media has driven significant advancements in numerical techniques. The surface integral equation (SIE) method provides a robust framework f ...

In the scope of rigorous sensor fusion in kinematic laser scanning, we present a qualitative improvement of an automated retrieval method of lidar-to-lidar 3D correspondences in terms of accuracy and speed, where correspondences are locally refined shifts ...

The"Breathing Lung-on-a-Chip,"a novel microfluidic device featuring a stretchable membrane, replicates the natural expansion and contraction of the human lung. It provides a more realistic in-vitro platform to study respiratory diseases, particle depositio ...

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 ...

Deep learning has brought incredible progress in areas where traditional algorithms struggle. It can be because they need rules that are sometimes hard to conceptualize, it can also be that the number and manner of combining these rules are too complex to ...

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to ext ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

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 ...

Nodal properties are of particular importance when investigating patterns in brain networks. Nodal information is usually studied by comparing a few nodal measurements (up to three), resulting in analyses in three-dimensional spaces, at maximum. We offer a ...

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

Rubble stone masonry is a common construction typology of historical city centres and vernacular architecture. While past earthquakes have shown that it is one of the most vulnerable masonry construction typologies, there are few experimental campaigns giv ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ...

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

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 ...

Research on irregular stone masonry walls is hampered by the lack of detailed geometrical models of their internal micro-structure, i.e. the shape and size of each stone and its position within the wall. Without such a geometric digital twin of walls teste ...

Steel frame structures are essential components of modern infrastructure. Understanding their behavior under seismic loading is critical for ensuring public safety and minimizing damage that occurs during earthquakes. To accurately predict the response of ...

Recent seismic events have showcased the vulnerability of non-structural components to even low- or moderate-intensity earthquakes that occur far more frequently than design-basis ones. Thus, community-critical buildings, such as hospitals, telecommunicati ...

Understanding seismic risk at both the national and sub-national level is essential for devising effective strategies and interventions aimed at its mitigation. The Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination ...

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

Chattez avec Graph Search

Modeling Light Scattering in Layered Media with the Surface Integral Equation Method

Generalization of point-to-point matching for rigorous optimization in kinematic laser scanning

Dynamics of nanoparticles in a 3D breathing lung-on-a-chip

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

Extending Capabilities of Attention-based Models

FSscore: A Personalized Machine Learning-Based Synthetic Feasibility Score

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

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

Connectome embedding in multidimensional graph spaces

Deep Learning for 3D Surface Modelling and Reconstruction

Experimental Investigation on Size-Effect of Rubble Stone Masonry Walls Under In-Plane Horizontal Loading: Overview and Preliminary Results

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Computational complexity of deep learning: fundamental limitations and empirical phenomena

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Digital Twins of Stone Masonry Buildings for Damage Assessment

Geometrical digital twins of the as-built microstructure of three-leaf stone masonry walls with laser scanning

A Softening Constitutive Law and Gradient-Inelastic Fiber-Based Element for 3-Dimensional Frame Simulations Under Seismic Excitations

The use of ductile steel fuses for the seismic protection of acceleration sensitive non- structural components: Numerical and Experimental verification

The Earthquake Risk Model of Switzerland, ERM-CH23