Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures.However,conventional manpower-based inspection methods not only incur considerable cost and time,but also cause frequent disputes regarding defects owing to poor inspections.Therefore,the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent,as the reduction in maintenance costs is significant from a long-term perspective.Thus,this paper proposes a deep learning-based image objectidentification method to detect the defects of paint peeling,leakage peeling,and leakage traces that mostly occur in underground parking lots made of concrete structures.The deep learning-based object-detection method can replace conventional visual inspection methods.A faster region-based convolutional neural network(R-CNN)model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects.The defects were classified according to their type,and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots.As a result,average precision scores of 37.76%,36.42%,and 61.29%were obtained for paint peeling,leakage peeling,and leakage trace defects,respectively.Thus,this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete

The durability performance of reinforced concrete(RC)building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration,thus,the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC structure.This study aims to predict the chloride ion diffusion coefficient of concrete,a heterogeneous material.A convolutional neural network(CNN)-based regression model that learns the condition of the concrete surface through deep learning,is developed to efficiently obtain the chloride ion diffusion coefficient.For the model implementation to determine the chloride ion diffusion coefficient,concrete mixes with w/c ratios of 0.33,0.40,0.46,0.50,0.62,and 0.68,are cured for 28 days;subsequently,the surface image data of the specimens are collected.Finally,the proposed model predicts the chloride ion diffusion coefficient using the concrete surface image data and exhibits an error of approximately 1.5E−12 m^(2)/s.The results suggest the applicability of proposed model to the field of facility maintenance for estimating the chloride ion diffusion coefficient of concrete using images.