Skip to main content

Generate MDOF lumped mass FE model using basic building information. Manupulate general OpenSees model to perform IDA and FEMA P-58 loss assessment.

Project description

MDOFModel

Generate Multi-Degree-Of-Freedom (MDOF) structures based on basic building information (such as floor area, number of stories, etc.), and perform dynamic analysis and economic loss assessment.

中文文档 (Chinese Documentation)

Project Introduction

MDOFModel is a Python library for Multi-Degree-Of-Freedom (MDOF) model analysis in structural engineering, primarily for seismic engineering analysis. This tool can generate lumped mass finite element models from basic building information and perform dynamic analysis, pushover analysis, loss assessment, and Incremental Dynamic Analysis (IDA).

Features

  • Structure Model Generation: Generate MDOF structural models based on basic building parameters (such as number of stories, floor area, structural type)
  • Seismic Dynamic Analysis: Perform linear and nonlinear time history analysis
  • Pushover Analysis: Implement structural static pushover analysis
  • Incremental Dynamic Analysis (IDA): Execute IDA analysis using FEMA P-695 far-field earthquake records
  • Loss Assessment: Conduct seismic loss assessment based on Hazus methodology

Installation Guide

Install this library using pip:

pip install MDOFModel

Usage Examples

Please refer to the Examples directory in this repository for detailed usage examples. We provide several ready-to-run scripts demonstrating different capabilities of MDOFModel:

  • Example1_ShearBuildingModel: A collection of examples demonstrating a simplified shear building model for:

    • 1_Dynamic.py: Time history dynamic analysis.
    • 2_Pushover.py: Static pushover analysis.
    • 3_LossAssessment.py: Economic loss assessment.
    • 4_IDA.py: Incremental Dynamic Analysis (IDA).
    • 5_EQSpectra.py: Earthquake spectra processing.
  • Example2_GeneralModel_Dynamic: Demonstrates how to perform dynamic time-history analysis on a general OpenSees structural model (e.g., 2D frame) using GeneralModelWrapper.

  • Example3_GeneralModel_Pushover: Demonstrates how to perform static pushover analysis on a general OpenSees structural model using GeneralModelWrapper.

  • Example4_GeneralModel_IDA: Demonstrates how to execute Incremental Dynamic Analysis (IDA) on a general OpenSees structural model using GeneralModelWrapper.

Main Modules Description

  • MDOF_CN: Multi-degree-of-freedom model generation based on Chinese codes
  • MDOF_LU: General multi-degree-of-freedom model generation
  • MDOFOpenSees: OpenSees interface for modeling and analysis
  • IDA: Incremental Dynamic Analysis
  • BldLossAssessment: Building loss assessment
  • Tool_IDA: IDA analysis auxiliary tools
  • Tool_LossAssess: Loss assessment auxiliary tools
  • ReadRecord: Earthquake record reading tool

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mdofmodel-0.8.1.tar.gz (7.5 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mdofmodel-0.8.1-py3-none-any.whl (7.4 MB view details)

Uploaded Python 3

File details

Details for the file mdofmodel-0.8.1.tar.gz.

File metadata

  • Download URL: mdofmodel-0.8.1.tar.gz
  • Upload date:
  • Size: 7.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for mdofmodel-0.8.1.tar.gz
Algorithm Hash digest
SHA256 1655413c8494f8fbc457befa7ab5559670fa93cecf206f42ec4ad1420e6e0380
MD5 097a49b5bf63322e71c6aca19c872843
BLAKE2b-256 fe7abe6567aee0b76463c2c610968bb5d6359f575cedcfce11544e862bc4ee9d

See more details on using hashes here.

File details

Details for the file mdofmodel-0.8.1-py3-none-any.whl.

File metadata

  • Download URL: mdofmodel-0.8.1-py3-none-any.whl
  • Upload date:
  • Size: 7.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for mdofmodel-0.8.1-py3-none-any.whl
Algorithm Hash digest
SHA256 ce6cd7412d4b4d1646c79dd4f4e34b0e52d9b7371e89d8db635524d76dfd395a
MD5 4535b0a40771c1c4f62b68471fcaa46e
BLAKE2b-256 9bb854aba88e8704f855c6aab45d58f92a3b95458391deb00e7e81279a612cd2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page