An Innovative Approach on Concrete Beam Renovation Based on Threshold Image Segmentation by Multivariate Analysis

EasyChair Preprint 6894

Abstract

 The building renovation process is the most time-consuming aspect. This paper grants the possibility of applying the multiple variable detections from segmentation of image components by a multivariate - technique using Convolutional function to assist automated reconstruction for the renovation of beam concrete. The research has two parts: the first one is multivariable dataset multivariate, with image segmentation using multiple threshold values are made for the proposed research, and the second is testing the data sets of segments, by using Convolutional functions applied for more images to make predictions. In the manual inspection, the sketch of the beam is prepared manually, and the conditions of the irregularities in the image are noted. Since the manual approach completely depends on the specialist's knowledge, manpower, and work pressure, it lacks objectivity in the quantitative analysis. So, automatic image segmentation with multi-thresholding based on Convolutional function is proposed as a renovating element to obtain a tool for the reconstruction of the image.

Keyphrases: Convolutional, image, multivariate, renovation, threshold

@booklet{EasyChair:6894,
  author    = {D Neguja and A Senthirajan},
  title     = {An Innovative Approach on Concrete Beam Renovation Based on Threshold Image Segmentation by Multivariate Analysis},
  howpublished = {EasyChair Preprint 6894},
  year      = {EasyChair, 2021}}