Structural Engineering Modules (Python 2.7 primarily)
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Updated
Apr 16, 2024 - Python
Structural Engineering Modules (Python 2.7 primarily)
Educational Vibration programs. Intended for undergraduate and early graduate students.
Python tools for interacting with, editing, and visualizing EPA SWMM5 models
Structural Analysis Library for Python based on the direct stiffness method
fatpack provides functions and classes for fatigue analysis of data series.
Advanced multiple object tracker using dlib and OpenCV.
GEstimator is a simple civil estimation software written in Python and GTK+. GEstimator can prepare estimates along with rate analysis and supports multiple databases.
A python module for structural analysis and design of different foundation type in accordance to the Eurocodes and the UK National Annex
Design, analysis, and optimization of structural trusses
slientruss3d : Python for stable truss analysis and optimization tool
A collection of open tools for Civil Engineers
Tools for calculating structural member cross sectional properties and a database of standard properties.
Civil engineering tools and algorithms.
Concrete-dam stability analyses with Python
Welcome to Blueprints, the cornerstone repository for civil engineering professionals and enthusiasts alike. Blueprints is a collaborative, open-source initiative designed to serve as a comprehensive library of code, tools, and best practices, aiming to streamline and innovate the workflows within the built environment sector.
Simple GUI application built using Tkinter, that simulates the template function of SAP2000 software, capable of rapid generation of various spatial structures
Learning based Image Scale Estimation (LISE) for Quantitative Visual Inspection
Currently working on an innovative Crack Detection and Analysis System, combining computer vision and machine learning to identify different types of wall cracks and provide tailored solutions. Demonstrating ongoing dedication to learning and problem-solving in the field of image processing.
Development of an AutoML System to Predict the Compressive Strength of Concrete
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