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7. Exploring temperature-resilient recycled aggregate concrete with waste rubber: An experimental and multi-objective optimization analysis

Author

Tang Yunchao, Wang Yufei, Wu Dongxiao, Chen Mengcheng, Pang Lan, Sun Junbo, Feng Wanhui, and Wang Xiangyu

Subjects
rubber-modified recycled aggregate, compressive strength, peak strain, machine learning, multi-objective optimization, Technology, Chemical technology, TP1-1185
Abstract

For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA) could be used to make rubber-modified recycled aggregate concrete (RRAC). The characteristics (compressive strength and peak strain) of RRAC with various amounts of RA and RPs after heating at various temperatures were studied in this work. The results show that high temperatures significantly decreased the uniaxial compressive strength (UCS), whereas the addition of RA (e.g., 50%) and RPs (e.g., 5%) can mitigate the negative effect caused by high temperatures. The peak strain can also be improved by increasing the replacement ratios of RA and RP. Support vector regression (SVR) models were trained using a total of 120 groups of UCS and peak strain experimental datasets, and an SVR-based multi-objective optimization model was proposed. The excellent correlation coefficients (0.9772 for UCS and 0.9412 for peak strain) found to illustrate the remarkable accuracy of the SVR models. The Pareto fronts of a tri-objective mixture optimization design (UCS, strain, and cost) were successfully generated as the decision reference at varying temperature conditions. A sensitivity analysis was performed to rank the importance of the input variables where temperature was found as the most important one. In addition, the replacement ratio of RA is more important compared with that of the RP for both the UCS and strain datasets. Among the mechanical properties of concrete, compressive strength and peak strain are two key properties. This study provides guidance for the study of RRAC constitutive models under high temperatures.

Published
2023
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9. An experimental investigation and machine learning-based prediction for seismic performance of steel tubular column filled with recycled aggregate concrete

Author

Tang Yunchao, Wang Yufei, Wu Dongxiao, Liu Zhonghe, Zhang Hexin, Zhu Ming, Chen Zheng, Sun Junbo, and Wang Xiangyu

Subjects
low-cycle reciprocating loading test, machine learning, recycled concrete-filled steel tube columns, slenderness ratio, seismic performance prediction, Technology, Chemical technology, TP1-1185
Abstract

This work presents the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns and 3 ordinary concrete-filled steel tube columns. Additionally, a systematic study on the influence of various parameters (e.g., slenderness ratio, axial compression ratio, etc.) was conducted on the seismic performance of the specimens. The results show that all the specimens have good hysteresis performance and a similar development trend of skeleton curve. The influence of slenderness ratio on the seismic index of the specimens is more significant than that of the axial compression ratio and the steel pipe wall thickness. Furthermore, artificial intelligence was applied to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, Random Forest with hyperparameters tuned by Firefly Algorithm was chosen. The high correlation coefficients (R) and low root mean square error values from the prediction results showed acceptable accuracy. In addition, sensitivity analysis was applied to rank the influence of the aforementioned input variables on the seismic performance of the specimens. The research results can provide experimental reference for the application of steel tube recycled concrete in earthquake areas.

Published
2022
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21. Combined effects of nano-silica and silica fume on the mechanical behavior of recycled aggregate concrete

Author

Chen Jieming, Tang Yunchao, Chen Zheng, Li Cong, Nong Yumei, and Feng Wanhui

Subjects
Technology, Materials science, Aggregate (composite), Silica fume, silica fume, Process Chemistry and Technology, Chemical technology, Physical and theoretical chemistry, QD450-801, Energy Engineering and Power Technology, Medicine (miscellaneous), nano-silica, TP1-1185, Surfaces, Coatings and Films, Biomaterials, microstructure analysis, Nano, mechanical behavior, Composite material, recycled aggregate concrete, Biotechnology
Abstract

Recycled aggregate concrete (RAC) is an environmentally friendly material. However, owing to inherent characteristics of the recycled aggregate (RA), it is difficult to promote and apply it in structural engineering. Silica fume (SF) and nano-silica (NS) have different characteristics as additives for RAC. It has been proven that adding SF only enhances the strength of RAC at a later stage, and NS can improve the early strength of RAC owing to its high pozzolanic activity. In this study, to further improve the properties of RAC, two types of additives were combined into RAC, which was named SF-NS-modified RAC (SSRAC). Compression and split tensile tests were conducted to analyze the mechanical properties of SSRAC at different curing ages. The results indicated that the combined addition of NS and SF improved the performance of RAC at early and later curing ages. Scanning electron microscopy and X-ray diffraction analyses were performed to explore the NS and SF mechanism. The results indicated that SF and NS in SSRAC had a good pozzolanic effect and underwent a secondary hydration reaction with calcium hydroxide to increase the production of calcium silicate hydrate, resulting in an increase in the properties of the interface transition zone. Finally, 6% SF and 2 or 3% NS are recommended as supplementary cementitious materials for RAC.

Published
2021

29. Grand Challenges of Machine-Vision Technology in Civil Structural Health Monitoring

Author

Lin Yunfan, Minghui Yao, Zou Xiangjun, Xueyu Huang, Tang Yunchao, and Huang Zhaofeng

Subjects
Engineering management, Engineering, Machine vision, business.industry, Deep learning, General Medicine, Artificial intelligence, Structural health monitoring, business, Civil infrastructure, Condition assessment, Grand Challenges, Computer technology
Abstract

Machine-vision technology has progressed remarkably in both accuracy and speed owing to advances in computer technology and artificial intelligence. In this paper, state-of-the-art research on vision-based techniques is reviewed for civil infrastructure condition assessment. The major challenges of machine vision technique in civil structural health monitoring are presented.

Published
2020

40. A Vision-Based Algorithm for Deformation of Steel Pipe Column Detection

Author

Chenglin Wang, Feng Wenxian, Wei-ming Situ, Yu-da Mo, Tang Yunchao, Jin-hui Li, and Xiangjun Zou

Subjects
Vision based, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Process (computing), Image processing, Deformation (meteorology), Column (database), Algorithm, Edge detection, Bridge (nautical), Image (mathematics)
Abstract

It has great significance for assessing the safety of engineering structures or the bridge by detecting Deformation of steel pipe column. The manual contact measurement is one of the traditional methods to detect the deformation of the steel pipe column. However, in the actual operation environment, the manual contact measurement is difficult and there are some hidden safety problems in the measurement process, thus it is particularly important to study a non-contact measurement method of the steel pipe column. Image processing is one of the common methods of non-contact measurement. In this paper, with the same steel pipe deformation image, respectively, using different edge detection algorithm, comparative analysis of the result, it come to a conclusion that the most suitable algorithm is Canny algorithm, and it is a classic algorithm of edge detection which has been applied in image processing. However, the traditional Canny algorithm has some defects, such as difficulty in threshold setting, low efficiency. Based on traditional Canny algorithm to improve it, this paper proposes an improved custom threshold Canny algorithm. The results show that the method proposed in this paper, the method of which is more than the traditional detection method to save at least 0.8s. Basically meet the requirements of real-time detection of stable and reliable, fast, high accuracy, and has a certain practicality.

Published
2017

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