Beast code in Giters

Penghui Zhang's repositories

MCAnalysis

This is an auto moment-curvature analysis tool. Mesh generation is accomplished by gmesh, and moment-curvature analysis is running in openseespy platform.

Language:Python13 30

fragility-analysising-using-python

This is a fragility analysising program for bridges.

Language:Python11 20

the-craft-of-selfteaching

One has no future if one couldn't teach themself.

Language:Jupyter Notebook200

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

Language:HTML100

prePostFEA

A general pre and post process framework for finite element analysis, such as ABAQUS, OpenSees et al.

Language:PythonGPL-3.0100

Cable-force-optimization-of-a-curved-cable-stayed-bridge

Cable force optimization of a curved cable-stayed bridge with combined simulated annealing method and cubic B-Spline interpolation curves

Language:TclMIT000

concrete-properties

Calculate section properties for reinforced concrete sections.

Language:PythonMIT000

Data-from-PEER

This code download ground motion records from PEER with given RSN numbers automatically

Language:Python000

Dynamics-Earthquake-Analysis-of-Structures

The main objectives of this individual project are: 1) to further enhance the understanding of the numerical time integration method – Newmark’s algorithm by numerically investigating its accuracy and stability, and by implementing it using a computer programming language (e.g. MatLab, C, C++, or Python); 2) to generate the earthquake response spectra from a particular earthquake; and 3) to undertake an earthquake analysis of a simple frame structure.

Apache-2.0000

Explainable_AL_Infrastructure_damage

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Ground-motion-generation-using-CWT

Ground motion generation using CWT

GPL-3.0000

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Apache-2.0000

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Apache-2.0000

Montecarlo-simulation-for-response-spectrum-simulation-and-ground-motion-selection

This jupyter notebook contains the code that is used to simulate response spectrum from target spectrum based on the monte-carlo simulation approach suggested by Jayaram, N., Lin, T., Eeri, M., & Baker, J. W. (2011). A Computationally efficient ground-motion selection algorithm for matching a target response spectrum mean and variance. Earthquake Spectra, 27(3), 1–22. https://doi.org/10.1193/1.3608002. The metadata and papers can be downloaded from this page https://web.stanford.edu/~bakerjw/gm_selection.html

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