Search Results for author:
Found 16 papers, 4 papers with code
A Modified da Vinci Surgical Instrument for OCE based Elasticity Estimation with Deep Learning
We propose modifying a da~Vinci surgical instrument to realize optical coherence elastography (OCE) for quantitative elasticity estimation.
LeanAI: A method for AEC practitioners to effectively plan AI implementations
This lack of understanding results in the disconnect between AI planning and implementation because the planning is based on a vision of what AI should solve without considering if it can or will solve it.
Points for Energy Renovation (PointER): A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics
Building point clouds in new regions can be generated with the open-source code published alongside the paper.
BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval
Efficient information retrieval (IR) from building information models (BIMs) poses significant challenges due to the necessity for deep BIM knowledge or extensive engineering efforts for automation.
Digital Twin: Where do humans fit in?
This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans.
Vitruvio: 3D Building Meshes via Single Perspective Sketches
If previous state-of-the-art (SOTA) data-driven methods for single view reconstruction (SVR) showed outstanding results in the reconstruction process from a single image or sketch, they lacked specific applications, analysis, and experiments in the AEC.
A new perspective on Digital Twins: Imparting intelligence and agency to entities
Despite the Digital Twin (DT) concept being in the industry for a long time, it remains ambiguous, unable to differentiate itself from information models, general computing, and simulation technologies.
Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data
Current methods to determine the energy efficiency of buildings require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete.
Digital Twin: From Concept to Practice
Recent technological developments and advances in Artificial Intelligence (AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT), virtually making it possible to introduce automation into all aspects of work processes.
Measuring the Impact of Blockchain and Smart Contract on Construction Supply Chain Visibility
The improvements were significantly more pronounced for inquiries that had higher product, trade, and temporal granularity; 2) blockchain-enabled solution was robust in the face of increased granularity, while the conventional solution experienced 50% and 66. 7% decline respectively in completeness and accuracy of information.
The Application of Blockchain-Based Crypto Assets for Integrating the Physical and Financial Supply Chains in the Construction & Engineering Industry
The paper contrasts the integration between cash and product flows in supply chains that rely on fiat currencies and crypto assets for their payment settlement.
Construction Payment Automation Using Blockchain-Enabled Smart Contracts and Reality Capture Technologies
This paper presents a smart contract-based solution for autonomous administration of construction progress payments.
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
In this paper, we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training.
3D Semantic Parsing of Large-Scale Indoor Spaces
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.
