钢纤维混凝土声发射参数设定与声源定位精度研究

doi:10.16552/j.cnki.issn1001-1625.2025.0873

摘要/Abstract

摘要：

针对钢纤维混凝土（SFRC）声发射（AE）检测过程中存在的参数设定与声源定位精度不足问题，本文设计并开展了系统试验研究。选取端钩型、剪切型和波纹型三种钢纤维，分别以0%、0.5%、1.0%、1.5%（体积分数）掺量制备试件，开展断铅试验和加载AE试验。结果表明，在水灰比等条件相同的情况下，钢纤维掺量对AE声速与信号上升时间影响显著，掺量每增加0.5个百分点，声速平均降低约4.3%，上升时间缩短约5.4%，钢纤维类型差异对参数影响较小。在传感器布置方式对比中，采用四点空间对角布置的方式表现出最小的定位误差和最佳稳定性。单轴压缩试验验证了所设定参数与布置方式的有效性，声源定位结果与实际裂缝位置高度吻合。本文提出了一套适用于SFRC的AE参数设定与传感器布置方式，为SFRC结构的损伤监测和健康评估提供了技术支持，具有重要的工程应用价值。

关键词: 钢纤维混凝土, 声发射, 声速设定, 时间参数, 传感器布置方式, 声源定位精度

Abstract:

A systematic experimental study was conducted to address the issues of parameter calibration and insufficient source localization accuracy in acoustic emission （AE） testing of steel fiber reinforced concrete （SFRC）. Three types of steel fiber—end-hook type， shear type， and corrugated type—were incorporated into specimens at volume fractions of 0%， 0.5%， 1.0%， and 1.5%， followed by pencil lead break tests and loading AE tests. The results show that under identical conditions such as water-cement ratio， the steel fiber content significantly influences AE sound velocity and signal rise time： for every 0.5 percentage points increase in steel fiber content， the average sound velocity decreases by approximately 4.3%， while the rise time shortens by about 5.4%. In contrast， the differences among fiber types have minimal effect on these parameters. Among the sensor layout schemes， a four-sensor spatial diagonal arrangement exhibits the smallest localization error and the best stability. Uniaxial compression tests verify the effectiveness of the proposed parameter settings and layout scheme， with source localization results closely matching actual crack positions. This study proposes a practical method for AE parameter calibration and sensor layout scheme in SFRC， providing technical support for damage monitoring and health assessment of SFRC structures， with significant engineering application value.

Key words: steel fiber reinforced concrete, acoustic emission, sound velocity calibration, time parameter, sensor layout scheme, source localization accuracy

中图分类号:

TU528.57

郭永民, 张龙娇, 安新正, 李伟志, 安树好, 纪梦琦, 王燕. 钢纤维混凝土声发射参数设定与声源定位精度研究[J]. 硅酸盐通报, 2026, 45(2): 471-481.

GUO Yongmin, ZHANG Longjiao, AN Xinzheng, LI Weizhi, AN Shuhao, JI Mengqi, WANG Yan. Acoustic Emission Parameter Calibration and Source Localization Accuracy in Steel Fiber Reinforced Concrete[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2026, 45(2): 471-481.

图/表 18

表1 粗骨料的物理性能

Table 1 Physical properties of coarse aggregate

Aggregate type	Porosity/%	Fragmentation index/%	Apparent density/（kg⋅m^-3）	Bulk density/（kg⋅m^-3）
Crushed stone	13.2	11.7	2 647	1 575

表2 钢纤维的主要性能

Table 2 Major properties of steel fiber

Fiber type	Fiber length/mm	Equivalent diameter/mm	Aspect ratio/mm	Tensile strength/MPa
End-hook type	25	0.83	30	≥600
Shear type	35	0.85	40	≥600
Corrugated type	38	1.11	34	≥600

图1 AE信号检测分析系统

Fig.1 AE signal detection and analysis system

图2 阈值断铅（PLB）试验示意图

Fig.2 Schematic diagram of threshold pencil lead break （PLB） test

图3 声速设定断铅试验

Fig.3 PLB test for sound velocity calibration

图4 时间参数设定断铅试验

Fig.4 PLB test for time parameter calibration

图5 5种待选传感器布置示意图

Fig.5 Layout schematic diagrams of five sensors to be selected

表3 不同试件AE信号幅值

Table 3 AE signal amplitudes of different specimens

Specimen	Lead break angle/（°）	Sensor 1 amplitude/dB	Standard deviation	Sensor 2 amplitude/dB	Standard deviation
T1	15	40.7	0.69	39.8	0.72
	30	41.2	1.42	40.4	1.58
	60	41.8	0.83	40.1	1.23
T2	15	42.5	1.55	41.6	0.91
	30	40.4	2.02	39.4	1.11
	60	41.7	1.54	39.9	1.53
T3	15	43.1	1.63	41.2	0.56
	30	43.5	1.98	40.9	0.93
	60	42.3	0.75	42.1	0.42
T4	15	40.9	0.63	38.7	0.78
	30	42.6	1.11	40.5	0.86
	60	41.3	0.96	40.7	0.85

图6 不同钢纤维掺量下声速对比

Fig.6 Comparison of sound velocity under different steel fiber content

表4 钢纤维掺量对声速影响的描述性统计结果

Table 4 Descriptive statistics on effect of steel fiber content on sound velocity

Steel fiber content/%	Average sound velocity/（m·s^-1）	Standard deviation	Coefficient of variation（C.V.）/%
0	3 595.44	24.32	0.68
0.5	3 449.80	78.43	2.27
1.0	3 359.70	97.46	2.90
1.5	3 261.14	111.96	3.54

表5 钢纤维掺量对声速影响的单因素方差分析结果

Table 5 Single factor analysis results of variance on effect of steel fiber content on sound velocity

Source of mutation	Sum of squares （SS）	degree of freedom （df）	Mean square （MS）	F-value	Significance （P）
Between groups （BG）	649 488.9	3	216 496.3	32.89	<0.001
Within groups （WG）	184 084.1	28	6 574.43
Total （T）	833 573.0	31

表6 声速均值的Tukey事后检验多重比较结果

Table 6 Tukey’s post hoc test results for multiple comparisons of mean sound velocity

Group （I）	Group （J）	Mean difference/（m·s^-1）	P-value	Significance （P<0.05）
0%	0.5%	145.64	<0.001	Yes
0%	1.0%	235.74	<0.001	Yes
0%	1.5%	434.30	<0.001	Yes
0.5%	1.0%	90.10	<0.001	Yes
0.5%	1.5%	288.66	<0.001	Yes
1.0%	1.5%	198.56	<0.001	Yes

图7 不同钢纤维掺量下时间对比

Fig.7 Comparison of time under different steel fiber content

图8 各种布置方式在各点误差平均值汇总图

Fig.8 Summary chart of average point-to-point error for various layout shemes

图9 加载与信号采集装置

Fig.9 Loading and signal acquisition device

表7 AE相关参数取值

Table 7 Values of parameters related to AE

Parameter	Threshold/dB	Sound velocity/（m·s^-1）	PDT/μs	HDT/μs	HLT/μs
Value	40	3 507	150	300	500

图10 荷载-累计AE参数时程关系曲线

Fig.10 Load-cumulative AE parameter time-history relationship curves

图11 AE源定位结果

Fig.11 AE source localization results

参考文献 27

[1]	RADOJIČIĆ V， RADULOVIĆ R， TARIĆ M， et al. The influence of the steel fibers on improvement of mechanical characteristic of concrete［J］. Mechanics Based Design of Structures and Machines， 2022， 50（8）： 2929-2939. DOI URL
[2]	SHI X J， PARK P， REW Y， et al. Constitutive behaviors of steel fiber reinforced concrete under uniaxial compression and tension［J］. Construction and Building Materials， 2020， 233： 117316. DOI URL
[3]	MU R， WEI L S， WANG X W， et al. Preparation of aligned steel fiber reinforced cementitious composite and its flexural behavior［J］. Journal of Visualized Experiments， 2018（136）： 56307.
[4]	BRANSTON J， DAS S， KENNO S Y， et al. Mechanical behaviour of basalt fibre reinforced concrete［J］. Construction and Building Materials， 2016， 124： 878-886. DOI URL
[5]	LEE M K， BARR B I G. An overview of the fatigue behaviour of plain and fibre reinforced concrete［J］. Cement and Concrete Composites， 2004， 26（4）： 299-305. DOI URL
[6]	BEHNIA A， CHAI H K， RANJBAR N， et al. Damage detection of SFRC concrete beams subjected to pure torsion by integrating acoustic emission and Weibull damage function［J］. Structural Control and Health Monitoring， 2016， 23（1）： 51-68. DOI URL
[7]	LI B， XU L H， CHI Y， et al. Experimental investigation on the stress-strain behavior of steel fiber reinforced concrete subjected to uniaxial cyclic compression［J］. Construction and Building Materials， 2017， 140： 109-118. DOI URL
[8]	CHISARI C， GUARNACCIA C， RIZZANO G. Numerical simulation of acoustic emission activity in reinforced concrete structures by means of finite element modelling at the macroscale［J］. Structural Health Monitoring， 2020， 19（2）： 537-551. DOI URL
[9]	SHIOTANI T. Evaluation of long-term stability for rock slope by means of acoustic emission technique［J］. NDT & E International， 2006， 39（3）： 217-228. DOI URL
[10]	AGGELIS D G， MPALASKAS A C， MATIKAS T E. Investigation of different fracture modes in cement-based materials by acoustic emission［J］. Cement and Concrete Research， 2013， 48： 1-8. DOI URL
[11]	VERSTRYNGE E， VAN STEEN C， VANDECRUYS E， et al. Steel corrosion damage monitoring in reinforced concrete structures with the acoustic emission technique： a review［J］. Construction and Building Materials， 2022， 349： 128732. DOI URL
[12]	SHARMA G， SHARMA S， SHARMA S K. Fracture monitoring of steel and GFRP reinforced concrete beams using acoustic emission and digital image correlation techniques［J］. Structural Concrete， 2021， 22（4）： 1962-1976. DOI URL
[13]	ZHANG H， LIU X Y， BAI L Y， et al. Acoustic emissions evaluation of the dynamic splitting tensile properties of steel fiber reinforced concrete under freeze-thaw cycling［J］. Frontiers of Structural and Civil Engineering， 2023， 17（9）： 1341-1356. DOI
[14]	OHTSU M. Progress in acoustic emission analysis for fracture mechanism in composite materials［J］. Cement and Concrete Research， 2023， 174： 107317.
[15]	ASHRAF S， RUCKA M. Comparative study on fracture evolution in steel fibre and bar reinforced concrete beams using acoustic emission and digital image correlation techniques［J］. Case Studies in Construction Materials， 2024， 20： e03359.
[16]	NIE Q P， LIU H N， MA T F， et al. Experimental study on acoustic emission of steel fiber broken pebble recycled concrete［J］. Construction and Building Materials， 2024， 452： 138942. DOI URL
[17]	刘洋，张亚梅，侯心宇，等. 钢纤维混凝土剪切损伤演化的声发射特性研究［J］. 土木工程学报， 2023， 56（增刊2）： 374-380.
	LIU Y， ZHANG Y M， HOU X Y， et al. Study on acoustic emission characteristics of shear damage evolution in steel fiber reinforced concrete［J］. Chinese Journal of Civil Engineering， 2023， 56（supplement 2）： 374-380 （in Chinese）.
[18]	BARBOSH M， LI B Q， SADHU A. Improved acoustic source localization method for crack identification in structures［J］. Applied Acoustics， 2024， 223： 110093. DOI URL
[19]	CHEN Z H， MIAO T J， LIU T， et al. Active-passive joint acoustic emission monitoring test considering the heterogeneity of concrete［J］. Materials， 2023， 16（24）： 7694. DOI URL
[20]	LIU T， FENG J， WANG Z， et al. Acoustic emission source localization in concrete structures using deep learning［J］. Structural Health Monitoring， 2022， 21（5）： 2341-2356.
[21]	陈建峰，王子健，王瑞芳，等. 基于改进粒子群算法的混凝土声发射源高精度定位方法［J］. 硅酸盐学报， 2022， 50（1）： 185-194.
	CHEN J F， WANG Z J， WANG R F， et al. High-precision localization method for concrete acoustic emission sources based on an improved particle swarm optimization algorithm［J］. Journal of the Chinese Ceramic Society， 2022， 50（1）： 185-194 （in Chinese）.
[22]	HASSAN F， MAHMOOD A K B， YAHYA N， et al. State-of-the-art review on the acoustic emission source localization techniques［J］. IEEE Access， 2021， 9： 101246-101266. DOI URL
[23]	中国工程建设标准化协会. 纤维混凝土试验方法标准：CECS 13—2009［S］. 北京：中国计划出版社， 2010.
	China Association for Engineering Construction Standardization. Standard test methods for fiber reinforced concrete：CECS 13—2009［S］. Beijing： China Planning Press， 2010 （in Chinese）.
[24]	戴金辉，袁靖. 单因素方差分析与多元线性回归分析检验方法的比较［J］. 统计与决策， 2016（9）： 23-26.
	DAI J H， YUAN J. Comparison of single-factor analysis of variance and multiple linear regression analysis methods［J］. Statistics and Decision Making， 2016（9）： 23-26 （in Chinese）.
[25]	BANKIR S， MURAT B. Experimental investigation and statistical evaluation of the effects of steel fiber aspect ratio and fiber rate on static and dynamic mechanical properties of concrete［J］. Construction and Building Materials， 2024， 414： 116790.
[26]	CHEN X L， WU Z， WANG H， et al. Statistical analysis and multi-objective optimization of the chloride ion penetration resistance of recycled aggregate concrete［J］. Cement and Concrete Composites， 2023， 137： 104928. DOI URL
[27]	中华人民共和国住房和城乡建设部，国家市场监督管理总局. 混凝土物理力学性能试验方法标准：GB/T 50081—2019［S］. 北京：中国建筑工业出版社， 2019.
	Ministry of Housing and Urban-Rural Development of the People’s Republic of China， State Administration for Market Regulation. Standard for test methods of concrete physical and mechanical properties： GB/T 50081—2019［S］. Beijing： China Architecture & Building Press， 2019 （in Chinese）.