OpenIPDM is a MATLAB open-source platform that stands for infrastructures probabilistic deterioration model

Overview

Open-Source Toolbox for Infrastructures Probabilistic Deterioration Modelling

OpenIPDM is a MATLAB open-source platform that stands for infrastructures probabilistic deterioration model. This software is developed to perform analyses on a network-scale visual inspection data, while accounting for the uncertainty associated with each inspector. The main application window in OpenIPDM enables assessing the structural deterioration behaviour and the effect of interventions at different levels starting from the structural element level up to the network level. OpenIPDM also include several toolboxes that facilitate performing verification and validation analyses on visual inspection data, in addition to learning model parameters. Furthermore, OpenIPDM has the capacity to handle missing data such as, missing interventions or missing structural attributes.

For tutorials, see: YouTube channel.

How to cite

OpenIPDM: A Probabilistic Framework for Estimating the Deterioration and Effect of Interventions on Bridges
Hamida, Z., Laurent, B. and Goulet, J.-A.
SoftwareX (Submitted, January 2022)

Prerequisites

  • Matlab (version 2020b or higher) installed on Mac OSX or Windows.

  • The Matlab Statistics and Machine Learning Toolbox is required.

  • Access to GPU computing (required only for Model Training toolbox)

  • Figures for LaTeX matlab2tikz (Optional)

Installation

  1. Download and extract the ZIP file or clone the git repository in your working directory.
  2. The working directory should include the following folders:
    • Scripts
    • Tools
    • Parameters
    • Network Data
    • Figures
    • ExtractedData
    • Help
  3. Double-click OpenIPDM.mlapp file to start MATLAB App Designer, and from the top ribbon in App Designer, click Run

Getting started

After starting OpenIPDM, the main user interface will open along with a message box to load the database. Note that the message box will not show up, if pre-processed data already exist in the folder Network Data. If you do not see anything except Matlab errors verify your Matlab version, and your Matlab path.

Input

OpenIPDM takes as an input two types of file formats

  1. '.csv': this file format is generally considered for the raw database.
  2. '.mat': for files containing model paramters and/or pre-processed database.

Output

OpenIPDM generally provides the following outputs:

  1. Deterioration state estimates.
  2. Service-life of an intervention.
  3. Effect of interventions.
  4. Synthetic time series of visual inspections.

Further details about the outputs can be found in the OpenIPDM documentation manual.

Remarks

The OpenIPDM package is originally developed based on the inspection and interventions database of the Transportation Ministry of Quebec (MTQ).

Built With

Contributing

Please read CONTRIBUTING.md for details on the process for submitting pull requests.

Authors

  • Zachary Hamida - Methodology, initial code and development - webpage
  • Blanche Laurent - Analytical inference for inspectors uncertainty - webpage
  • James-A. Goulet - Methodology - webpage

License

This project is licensed under the MIT license - see the LICENSE file for details

Acknowledgments

Owner
CIVML
CIVML
The code for paper "Learning Implicit Fields for Generative Shape Modeling".

implicit-decoder The tensorflow code for paper "Learning Implicit Fields for Generative Shape Modeling", Zhiqin Chen, Hao (Richard) Zhang. Project pag

Zhiqin Chen 353 Dec 30, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

IIC2233 @ UC 0 Dec 15, 2022
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud This repository contains a reference implementation of our Part-Aware Data Augment

Jaeseok Choi 62 Jan 03, 2023
Official PaddlePaddle implementation of Paint Transformer

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [Paddle Implementation] Update We have optimized the serial inference p

TianweiLin 284 Dec 31, 2022
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
Converts given image (png, jpg, etc) to amogus gif.

Image to Amogus Converter Converts given image (.png, .jpg, etc) to an amogus gif! Usage Place image in the /target/ folder (or anywhere realistically

Hank Magan 1 Nov 24, 2021
Simple and ready-to-use tutorials for TensorFlow

TensorFlow World To support maintaining and upgrading this project, please kindly consider Sponsoring the project developer. Any level of support is a

Amirsina Torfi 4.5k Dec 23, 2022
Code and data accompanying our SVRHM'21 paper.

Code and data accompanying our SVRHM'21 paper. Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0 to be installed. Python scripts i

5 Nov 17, 2021
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"

DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is

Kevin Roitero 2 Dec 15, 2022
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability

PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability PCACE is a new algorithm for ranking neurons in a CNN architecture in order

4 Jan 04, 2022
Robust Consistent Video Depth Estimation

[CVPR 2021] Robust Consistent Video Depth Estimation This repository contains Python and C++ implementation of Robust Consistent Video Depth, as descr

Facebook Research 213 Dec 17, 2022
Official implementation of Long-Short Transformer in PyTorch.

Long-Short Transformer (Transformer-LS) This repository hosts the code and models for the paper: Long-Short Transformer: Efficient Transformers for La

NVIDIA Corporation 198 Dec 29, 2022
[Nature Machine Intelligence' 21] "Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence"

[UCADI] COVID-19 Diagnosis With Federated Learning Intro We developed a Federated Learning (FL) Framework for global researchers to collaboratively tr

HUST EIC AI-LAB 30 Dec 12, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans Introduction We introduce the task of dense captioning in 3D scans from commodity RGB-D sensor

Dave Z. Chen 79 Nov 07, 2022
An onlinel learning to rank python codebase.

OLTR Online learning to rank python codebase. The code related to Pairwise Differentiable Gradient Descent (ranker/PDGDLinearRanker.py) is copied from

ielab 5 Jul 18, 2022
ZEBRA: Zero Evidence Biometric Recognition Assessment

ZEBRA: Zero Evidence Biometric Recognition Assessment license: LGPLv3 - please reference our paper version: 2020-06-11 author: Andreas Nautsch (EURECO

Voice Privacy Challenge 2 Dec 12, 2021