Deep learning enabled particle analysis for quality assurance of construction materials

Zeng，Ziyue1,2,3,5; Wei，Yongqi1,2; Wei，Zhenhua3; Yao，Wu1,2; Wang，Changying1,2; Huang，Bin4; Gong，Mingzi4; Yang，Jiansen5

2022-08-01

10.1016/j.autcon.2022.104374

AUTOMATION IN CONSTRUCTION   影响因子和分区

0926-5805

1872-7891

Microspheres in fly ash are critically important for determining the properties and performance of fly ash dosed concrete, but facile and cost-effective analysis and quality control of fly ash microspheres remain difficult on construction sites. This paper describes a deep learning method to segment and analyze fly ash spherical particles using a simple optical microscope and a path aggregation network. The proposed method accurately detects microspheres and predicts their particle size distribution and volume fraction, outperforming traditional methods for particle analysis. The predicted results are directly linked to key properties that determine the quality of fly ash. This research establishes an automated and efficient method for rapid job-site fly ash spherical particle analysis, so that inexpensive and handy construction material quality control and assurance can be achieved for infrastructure construction.

Deep learning Fly ash Image segmentation Microsphere Particle size distribution

SCI ; EI

英语

通讯

National Key Research and Development Program of China[2019YFC1906203];National Natural Science Foundation of China[51468002];

Construction & Building Technology ; Engineering

Construction & Building Technology ; Engineering, Civil

WOS:000808452200003

ELSEVIER

20222212168534

Cost effectiveness ; Deep learning ; Fly ash ; Light transmission ; Microspheres ; Particle size ; Particle size analysis ; Quality assurance ; Quality control ; Size distribution

Ergonomics and Human Factors Engineering:461.4 ; Light/Optics:741.1 ; Industrial Economics:911.2 ; Quality Assurance and Control:913.3 ; Mathematical Statistics:922.2 ; Materials Science:951

ENGINEERING

2-s2.0-85130701010

Scopus

1.Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education,Tongji University,Shanghai,201804,China
2.School of Materials Science and Engineering,Tongji University,Shanghai,201804,China
3.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.CCCC First Highway Xiamen Engineering Co.,Ltd,Xiamen,361021,China
5.School of Civil and Hydraulic Engineering,Ningxia University,Ningxia,750021,China

Zeng，Ziyue,Wei，Yongqi,Wei，Zhenhua,et al. Deep learning enabled particle analysis for quality assurance of construction materials[J]. AUTOMATION IN CONSTRUCTION,2022,140.

Zeng，Ziyue.,Wei，Yongqi.,Wei，Zhenhua.,Yao，Wu.,Wang，Changying.,...&Yang，Jiansen.(2022).Deep learning enabled particle analysis for quality assurance of construction materials.AUTOMATION IN CONSTRUCTION,140.

Zeng，Ziyue,et al."Deep learning enabled particle analysis for quality assurance of construction materials".AUTOMATION IN CONSTRUCTION 140(2022).