Article(id=1274300228853359435, tenantId=1146029695717560320, journalId=1272208980697911299, issueId=1274300092707266809, articleNumber=null, orderNo=null, doi=10.3724/1000-6915.jrme.2025.0471, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751472000000, receivedDateStr=2025-07-03, revisedDate=1759939200000, revisedDateStr=2025-10-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1781746449912, onlineDateStr=2026-06-18, pubDate=1769875200000, pubDateStr=2026-02-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1781746449912, onlineIssueDateStr=2026-06-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1781746449912, creator=13701087609, updateTime=1781746449912, updator=13701087609, issue=Issue{id=1274300092707266809, tenantId=1146029695717560320, journalId=1272208980697911299, year='2026', volume='45', issue='2', pageStart='321', pageEnd='638', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1781746417452, creator=13701087609, updateTime=1781746463571, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1274300286466335306, tenantId=1146029695717560320, journalId=1272208980697911299, issueId=1274300092707266809, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1274300286466335307, tenantId=1146029695717560320, journalId=1272208980697911299, issueId=1274300092707266809, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=381, endPage=396, ext={EN=ArticleExt(id=1274300229096629069, articleId=1274300228853359435, tenantId=1146029695717560320, journalId=1272208980697911299, language=EN, title=Research on the evolution law of microcracks in sandstone based on rectangular tensor inversion and RA-AF analysis, columnId=null, journalTitle=Chinese Journal of Rock Mechanics and Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To investigate the mesoscopic crack evolution behavior and the dominant mechanisms during the failure process of sandstone, an integrated approach combining acoustic emission moment tensor inversion with RA-AF parameter analysis was employed to quantitatively characterize the types, spatiotemporal distribution, and stress response characteristics of microcracks. Based on moment tensor theory and incorporating sensor coupling coefficients calibrated through pencil-lead break experiments, microcracks were classified into five categories—shear cracks, tensile-shear mixed-mode cracks, compressive-shear mixed-mode cracks, tensile cracks, and compressive cracks—using the crack tensile angle criterion. Furthermore, an RA-AF empirical model was established to support the analysis. The results indicate the following: (1) Under various loading paths, microcracks resulting from sandstone failure are predominantly shear cracks. The number of each of the five microcrack types exhibits a positive correlation with stress level, with shear cracks showing the most significant increase. (2) As stress increases, microcracks initiate, propagate, and gradually coalesce, forming a fracture zone that corresponds to the macroscopic failure surface. (3) RA-AF analysis reveals that shear cracks account for more than 50% of all microcracks in sandstone, which aligns with findings from moment tensor inversion. (4) Waveforms generated by tensile cracks exhibit abrupt characteristics, with concentrated signal energy in the frequency domain, whereas waveforms associated with shear cracks display oscillatory behavior, featuring dispersed frequency-domain energy and higher amplitude. This distinction provides a physical mechanism that explains the heterogeneity observed in RA-AF parameters. (5) Moment tensor inversion is well-suited for theory-driven, detailed analysis of crack mechanisms, while RA-AF analysis is more appropriate for rapid identification of crack types in engineering practice. This study elucidates the dominant micromechanical mechanism of shear failure in sandstone and the co-evolutionary behavior of multiple crack types, thereby providing a theoretical foundation for rock fracture prediction.

, correspAuthors=Gang LIU, authorNote=null, correspAuthorsNote=
* LIU Gang (1986–), associate professor, is engaged in research work in the field of rock mechanics in mines. E-mail:
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为揭示砂岩破坏全过程的细观裂纹演化规律及主导机制,研究融合声发射矩张量反演与RA-AF参数分析技术,定量解析微裂纹类型、时空分布及应力响应特征。基于矩张量理论,结合断铅试验标定的传感器耦合系数,采用裂纹张拉角将微裂纹划分为剪切裂纹、拉剪混合裂纹、压剪混合裂纹、张拉裂纹及压缩裂纹5种,并建立RA- AF经验模型进行分析。结果表明:(1)在不同加载路径下,砂岩破坏产生的微裂纹均以剪切裂纹为主,且5种微裂纹数量均与应力水平呈正相关关系,其中剪切裂纹数量增长最显著;(2)随着应力的增加,微裂纹萌生、聚集,最终贯通形成与宏观破坏面吻合的破裂带;(3)通过RA-AF分析得到砂岩剪切裂纹占比均超过50%,与矩张量反演结果一致;(4)张拉裂纹产生的波形呈突发特征,信号能量频域集中;剪切裂纹产生的波形呈振荡特征,能量频域分散且幅值更高,从物理机制上解释RA-AF参数的分异性。(5)矩张量反演适用于理论驱动的精细裂纹类型机制解析,RA-AF分析适用于工程现场快速判断裂纹类型。研究阐明砂岩剪切破坏的微观主导机制与多类型裂纹协同演化规律,为岩石破裂预警提供了理论依据。

, correspAuthors=刘刚, authorNote=null, correspAuthorsNote=
* 刘刚(1986–),现任副教授,主要从事矿山岩石力学方面的研究工作。E-mail:
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results, figureFileSmall=+DIHSYbFzhsMk71z+vrQMQ==, figureFileBig=KoUQN+YXleFMVqT65b+RbQ==, tableContent=null), ArticleFig(id=1274368994724307721, tenantId=1146029695717560320, journalId=1272208980697911299, articleId=1274300228853359435, language=EN, label=null, caption=null, figureFileSmall=s1p8Mh9TBUzxF56VKtnkcA==, figureFileBig=jOpLVB8PilFmivg6YB9COQ==, tableContent=null), ArticleFig(id=1274368994829165322, tenantId=1146029695717560320, journalId=1272208980697911299, articleId=1274300228853359435, language=CN, label=Fig.16, caption=Typical waveform diagram of shear crack generation and its spectrum analysis results, figureFileSmall=s1p8Mh9TBUzxF56VKtnkcA==, figureFileBig=jOpLVB8PilFmivg6YB9COQ==, tableContent=null), ArticleFig(id=1274368994975965963, tenantId=1146029695717560320, journalId=1272208980697911299, articleId=1274300228853359435, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Tension angleγ/( °)Microcrack typeCriterion standard
60~90Tensile crackThe movement direction vector l tends to be inclined toward the normal direction of the crack surface, indicating that the crack is primarily dominated by normal tensile stress.
30~60Tensile-shear mixed crackAs the angle between the motion direction vector l and the normal direction increases, the tangential slip component is significantly enhanced, but the normal separation component still dominates.
-30~30Shear crackThe movement direction vector l is almost entirely within the plane of the crack surface, nearly perpendicular to the normal direction.
-60~-30Compressive-shear mixed cracksThe motion direction vector l shows components opposite to the normal, indicating that the crack process is significantly affected by normal compressive stress, while also accompanied by noticeable tangential slip.
-90~-60Compression crackThe motion direction vector l points inward toward the crack surface, indicating that the crack is dominated entirely by normal compressive stress.
), ArticleFig(id=1274368995080823564, tenantId=1146029695717560320, journalId=1272208980697911299, articleId=1274300228853359435, language=CN, label=Table 1, caption=

Tension angle γ and its corresponding fracture type[19]

, figureFileSmall=null, figureFileBig=null, tableContent=
Tension angleγ/( °)Microcrack typeCriterion standard
60~90Tensile crackThe movement direction vector l tends to be inclined toward the normal direction of the crack surface, indicating that the crack is primarily dominated by normal tensile stress.
30~60Tensile-shear mixed crackAs the angle between the motion direction vector l and the normal direction increases, the tangential slip component is significantly enhanced, but the normal separation component still dominates.
-30~30Shear crackThe movement direction vector l is almost entirely within the plane of the crack surface, nearly perpendicular to the normal direction.
-60~-30Compressive-shear mixed cracksThe motion direction vector l shows components opposite to the normal, indicating that the crack process is significantly affected by normal compressive stress, while also accompanied by noticeable tangential slip.
-90~-60Compression crackThe motion direction vector l points inward toward the crack surface, indicating that the crack is dominated entirely by normal compressive stress.
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基于矩张量反演与RA-AF分析砂岩微裂纹演化规律研究
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刘刚 1, 2, * , 石翔 1, 2 , 昝永龙 1, 2 , 王盛旋 1, 2
岩石力学与工程学报 | 理论与试验研究 2026,45(2): 381-396
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岩石力学与工程学报 | 理论与试验研究 2026, 45(2): 381-396
基于矩张量反演与RA-AF分析砂岩微裂纹演化规律研究
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刘刚1, 2, * , 石翔1, 2, 昝永龙1, 2, 王盛旋1, 2
作者信息
  • 1.黑龙江科技大学 矿业工程学院,黑龙江 哈尔滨 150022
  • 2.黑龙江科技大学 黑龙江省煤矿深部开采地压控制与瓦斯治理重点实验室,黑龙江 哈尔滨 150022

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* 刘刚(1986–),现任副教授,主要从事矿山岩石力学方面的研究工作。E-mail:
Research on the evolution law of microcracks in sandstone based on rectangular tensor inversion and RA-AF analysis
Gang LIU1, 2, * , Xiang SHI1, 2, Yonglong ZAN1, 2, Shengxuan WANG1, 2
Affiliations
  • 1.School of Mining Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang 150022, China
  • 2.Heilongjiang Provincial Key Laboratory of Ground Pressure Control and Gas Control in Deep Coal Mine Mining, Heilongjiang University of Science and Technology, Harbin, Heilongjiang 150022, China
出版时间: 2026-02-01 doi: 10.3724/1000-6915.jrme.2025.0471
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为揭示砂岩破坏全过程的细观裂纹演化规律及主导机制,研究融合声发射矩张量反演与RA-AF参数分析技术,定量解析微裂纹类型、时空分布及应力响应特征。基于矩张量理论,结合断铅试验标定的传感器耦合系数,采用裂纹张拉角将微裂纹划分为剪切裂纹、拉剪混合裂纹、压剪混合裂纹、张拉裂纹及压缩裂纹5种,并建立RA- AF经验模型进行分析。结果表明:(1)在不同加载路径下,砂岩破坏产生的微裂纹均以剪切裂纹为主,且5种微裂纹数量均与应力水平呈正相关关系,其中剪切裂纹数量增长最显著;(2)随着应力的增加,微裂纹萌生、聚集,最终贯通形成与宏观破坏面吻合的破裂带;(3)通过RA-AF分析得到砂岩剪切裂纹占比均超过50%,与矩张量反演结果一致;(4)张拉裂纹产生的波形呈突发特征,信号能量频域集中;剪切裂纹产生的波形呈振荡特征,能量频域分散且幅值更高,从物理机制上解释RA-AF参数的分异性。(5)矩张量反演适用于理论驱动的精细裂纹类型机制解析,RA-AF分析适用于工程现场快速判断裂纹类型。研究阐明砂岩剪切破坏的微观主导机制与多类型裂纹协同演化规律,为岩石破裂预警提供了理论依据。

岩石力学  /  声发射  /  矩张量反演  /  RA-AF  /  裂纹类型  /  波形分析

To investigate the mesoscopic crack evolution behavior and the dominant mechanisms during the failure process of sandstone, an integrated approach combining acoustic emission moment tensor inversion with RA-AF parameter analysis was employed to quantitatively characterize the types, spatiotemporal distribution, and stress response characteristics of microcracks. Based on moment tensor theory and incorporating sensor coupling coefficients calibrated through pencil-lead break experiments, microcracks were classified into five categories—shear cracks, tensile-shear mixed-mode cracks, compressive-shear mixed-mode cracks, tensile cracks, and compressive cracks—using the crack tensile angle criterion. Furthermore, an RA-AF empirical model was established to support the analysis. The results indicate the following: (1) Under various loading paths, microcracks resulting from sandstone failure are predominantly shear cracks. The number of each of the five microcrack types exhibits a positive correlation with stress level, with shear cracks showing the most significant increase. (2) As stress increases, microcracks initiate, propagate, and gradually coalesce, forming a fracture zone that corresponds to the macroscopic failure surface. (3) RA-AF analysis reveals that shear cracks account for more than 50% of all microcracks in sandstone, which aligns with findings from moment tensor inversion. (4) Waveforms generated by tensile cracks exhibit abrupt characteristics, with concentrated signal energy in the frequency domain, whereas waveforms associated with shear cracks display oscillatory behavior, featuring dispersed frequency-domain energy and higher amplitude. This distinction provides a physical mechanism that explains the heterogeneity observed in RA-AF parameters. (5) Moment tensor inversion is well-suited for theory-driven, detailed analysis of crack mechanisms, while RA-AF analysis is more appropriate for rapid identification of crack types in engineering practice. This study elucidates the dominant micromechanical mechanism of shear failure in sandstone and the co-evolutionary behavior of multiple crack types, thereby providing a theoretical foundation for rock fracture prediction.

rock mechanics  /  acoustic emission  /  moment tensor inversion  /  RA-AF  /  type of rupture  /  waveform analysis
刘刚, 石翔, 昝永龙, 王盛旋. 基于矩张量反演与RA-AF分析砂岩微裂纹演化规律研究. 岩石力学与工程学报, 2026 , 45 (2) : 381 -396 . DOI: 10.3724/1000-6915.jrme.2025.0471
Gang LIU, Xiang SHI, Yonglong ZAN, Shengxuan WANG. Research on the evolution law of microcracks in sandstone based on rectangular tensor inversion and RA-AF analysis[J]. Chinese Journal of Rock Mechanics and Engineering, 2026 , 45 (2) : 381 -396 . DOI: 10.3724/1000-6915.jrme.2025.0471
砂岩作为矿山巷道、地下洞室及边坡工程中的关键承载岩体,其内部微裂纹的演化行为直接决定了工程结构的长期稳定性与灾害发生的临界阈值[1]。此类灾害往往具有突发性和前兆隐蔽性,其物理本质是微裂纹在应力作用下的不可逆累积所诱发的渐进式失稳。然而,传统监测手段(如位移计、应力计)受限于空间分辨率和采样频率,难以有效捕捉毫米级以下的损伤萌生过程,导致预警信号滞后于灾变临界点。因此,实现岩体失稳的精准预警与灾害防控,亟需从微观尺度深入揭示岩石从损伤累积到宏观破裂的内在机制。声发射(acoustic emission,AE)技术通过实时捕捉岩石微裂纹释放的弹性波,为损伤演化提供了动态透视窗口与预警信号源,每个AE事件本质上就是一个微震源,其携带的震源参数(如位置、破裂模式、能量释放率等)与天然地震具有物理同源性,这种相似性使得地震学领域具有成熟的震源机制解析方法,特别是矩张量理论,可被有效借鉴应用于岩石细观破裂机制研究,为揭示岩石破坏的本质和演化规律提供了强有力的工具[2]
震源机制定量解析的核心工具是矩张量理论。F. Gilbert[3]于1970年首次将矩张量定义为作用在一点上等效体力的一阶矩,该理论通过一个二阶对称张量(含6个独立分量)完整描述震源的力学性质。矩张量反演理论可被应用于岩石声发射数据的分析中,用于揭示岩石的微破裂机制[4]。B. Feignier和R. P. Young[5]运用矩张量理论分析震源破裂机制,将矩张量分解为纯剪切裂纹成分(MCD)、各向同性成分(MISO)和补偿线性矢量偶极成分(MCLVD),依据各向同性成分(MISO)所占比重来量化其裂纹类型。M. Ohtsu[6]在进行室内声发射量化分析时,根据矩张量特征值中纯剪切裂纹成分所占比重,判断声发射事件裂纹类型,分析了岩石破裂方位等相关信息。秦子森和郭宦富[7]开展了直剪条件下不同粒径花岗岩的声发射试验,发现不同粒径花岗岩的微裂纹均以剪切裂纹为主,并且随着粒径的增大,剪切裂纹类型占比逐渐升高。李江哲[8]通过结合声发射试验,对花岗岩、红砂岩和白砂岩3种不同岩性岩石的破裂特征进行了探究,并对采集到的声发射信号进行了时频域分析和矩张量反演处理。研究发现3种不同岩性试样在加载过程中,张拉裂纹占比呈现先增加后减少的趋势,拉剪混合裂纹和剪切裂纹占比则呈现先减少后增加的趋势。另外,基于AE波形特征参数的经验方法,如RA-AF参数分析法,因其简便性在工程现场得到应用,用于快速区分张拉裂纹与剪切裂纹。王桂林等[9]利用单轴压缩试验下声学特征(RA-AF)对“裂隙”、“椭圆孔”、“类蘑菇孔”和“类哑铃孔”4类溶隙形状的灰岩微裂纹演化机制进行了研究。葛振龙等[10]研究了高温后砂岩内部不同类型裂纹的发展演化规律。李江哲和南博文[11]研究了绢英母石英岩、绿泥石石英片岩和黑云母石英片岩3种岩样破坏过程中细观演化特征及裂纹类型。王聚贤等[12]通过kneedle算法,形成了精确的RA-AF分界线方程的方法。
尽管矩张量反演在岩石破裂分析中广泛应用,现有研究仍存在以下空白:(1)针对砂岩破坏全过程,不同类型微裂纹,特别是拉、压、剪及其混合模式的精细分类、动态时空演化规律及其与宏观破坏面形成的关联机制,缺乏系统深入的追踪与可视化分析;(2)时空演化分析多聚焦静态分布或峰值阶段,缺乏砂岩破坏全过程中5种裂纹的动态协同图谱,难以揭示宏观剪切带的贯通机制;(3)混合裂纹与主剪切机制的协同规律未被量化——其增长是否同步?这缺乏微裂纹数量–应力演化曲线佐证,这使得工程预警缺乏类型化依据。(4)对单轴加载中不同加载路径(单调加载和循环加卸载)来判断砂岩裂纹类型的方面研究较少。(5)现有研究较少交叉验证矩张量反演与声发射RA-AF参数分析的分类结果,缺乏对2种方法适用场景的系统评估,尤其缺乏结合波形物理特征对参数分类机制的解释。针对上述空白,本文提出系统性研究方案:(1)基于裂纹张拉角γ建立5种裂纹精细分类准则,实现混合裂纹独立追踪;(2)通过origin软件构建不同应力水平下5种裂纹空间动态演化模型;(3)绘制5种裂纹事件数–应力响应曲线,定量揭示剪切裂纹快速增长与拉剪、压剪混合裂纹缓慢增长的协同规律;(4)对单轴加载中不同加载路径进行裂纹类型判断。(5)同步应用RA-AF参数分析法建立经验分类模型,对比矩张量反演结果一致性,并提取典型张拉、剪切裂纹波形分析其时频特征,包括上升时间、幅值、频谱等,揭示RA-AF参数差异的物理本质。该成果将矩张量反演升级为可预警的灾害预测引擎,推动岩石破坏力学向精准干预迈进。
为定量解析岩石微裂纹的力学机制与类型,本文采用2种核心方法:基于弹性波理论的矩张量反演(moment tensor inversion)和基于声发射波形特征参数的RA-AF参数分析法。本节将系统阐述这2种方法的理论基础、关键计算步骤及裂纹类型分类准则。
根据弹性波动力学理论,介质中某点xt时刻因裂纹扩展产生的位移ui(xt)可表示[13]
ui(xt)=Mjk[Gijk(xx0t)]S(t)
式中:Gijk(xx0t)为格林函数的空间导数,描述了介质从源点x0到接收点x的波传播效应;S(t)为震源时间函数;Mjk为声发射源矩张量,是对声发射源力学特性的描述。
M. Ohtsu[6]引入脉冲函数而忽略时间函数S(t),将矩张量反演方法进行了简化处理,假定振动波的传播介质为各向同性的均匀介质,则只采用震源定位与P波初动振幅即可求解矩张量,P波在声发射探头处的初动振幅u(x)[14]
u(x)=CsRe(tr)R(r1r2r3)[m11m12m13m21m22m23m31m32m33]{r1r2r3}
式中:Cs为传感器耦合系数(需在试验前,用断铅试验确定);R为声发射事件与传感器之间的距离;r1r2r3分别为其方向余弦矩阵中的xyz分量;Re(tr)为震源波的反射系数,其计算公式如下[15]
Re(tr)=2k2a[k22(1a2)][k22(1a2)]2+4a(1a2)k21+a2
式中:k为纵横波波速比,k=vp/vst为界面法向方向;r为波的入射方向;atr的内积。特别地,当信号波由垂直方向到达试样表面时,a = 1,Re(tr) = 2。
声发射信号P波初动振幅的精确提取是进行矩张量反演的基础与关键。在确定声发射P波到时(即P波初到时间)后,需识别紧随其后的第一个振幅极值点(波峰或波谷),该幅值就是P波初动振幅[16],如图1所示。
传感器耦合系数Cs反映了传感器与试样表面的接触状态及其灵敏度,是准确获取P波初动振幅A(x)的关键参数,需通过断铅试验标定。标定方法如下:
(1)在试样表面选取多个已知空间坐标的预设点,使用标准断铅装置进行断铅操作。断铅时,铅笔与试样表面保持45°夹角,该角度可确保断铅产生的弹性波中拉应力与压应力分量相等,满足对称性条件,有助于提高标定的一致性。
(2)记录每次断铅时,每个传感器接收到的P波初动振幅A(x)。
(3)利用以下公式计算每个传感器在断铅点处的Cs[17]
Cs=A(x)RRe(tr)
式中:R为断铅点到各个传感器之间的距离。
对于每一个被有效定位的声发射事件,要求至少被6个传感器记录到P波初动振幅(矩张量有6个独立分量),利用式(2)为每个传感器建立方程,构成一个以矩张量M的6个独立分量为未知数的超定方程组。采用最小二乘法求解此方程组,即可获得该声发射事件对应的完整矩张量M
求解出矩张量后,进行特征值分解,得到特征值M1M2M3及其对应的特征向量e1e2e3,其中,M1>M2>M3,根据特征值和特征向量可以计算出裂纹面的法向量n和运动方向向量l,计算式分别为
n=M1M2M1M3e1M2M3M1M3e3
l=M1M2M1M3e1+M2M3M1M3e3
值得注意的是,破裂面主应力状态对向量nl的影响是微裂纹机制分析的核心物理基础,本文所采用的矩张量理论框架包含了这一机制。矩张量M作为震源处等效体力对的数学表达,综合反映了微裂纹事件的力学机制、强度及其所处的局部应力环境[18]。对矩张量进行特征值分解后,其最大特征值M1的方向(特征向量e1)代表了局部最大主压应力的方向,而最小特征值M3的方向(特征向量e3)则与最小主压应力(即最大主张应力)方向一致。本文中的式(5)与(6)并非独立于主应力状态的几何假设,而是基于上述矩张量分解结果的直接数学推导。由此得到的裂纹面法向量n和运动方向向量l,本质上是该局部主应力场作用下最可能的破裂几何参数。换言之,本文通过矩张量反演,首先隐含地确定了主应力的方向和相对大小,继而才推导出在此应力状态下应产生的裂纹类型。这使得无需对主应力方向进行任何先验假设,尤其适用于各向异性岩石材料内部复杂多变的微观应力场。
为定量区分不同类型的微裂纹(张拉、压缩、剪切及其混合),本文引入张拉角γ作为核心判据。张拉角γ定义为运动方向向量l与其在裂纹面上的投影向量之间的夹角,其计算公式为
γ=90°arccos(nl)180°π
张拉角γ的数值是判断微裂纹类型的物理核心,其大小直接反映了微裂纹事件中运动方向向量l相对于裂纹面法向量n的取向,从而揭示了主导的力学机制。为对破裂机制进行精细化研究,需将简单的“张拉–剪切”二分法进一步细化。基于张拉角γ的连续取值范围所表征的破裂机制差异,本文将微裂纹定量划分为5种类型,如表1[19]所示,其中,“压缩裂纹”是指在微观尺度上,由法向压应力绝对主导、导致局部体积收缩的破坏事件,其物理机制主要包括矿物颗粒的压碎、原生微孔隙的坍塌或胶结物的破碎。在矩张量表征上,该类事件具有负的各向同性成分(MISO)和负的张拉角γ,表明其运动方向向量l指向裂纹面内部。引入此分类旨在更完备地描述矩张量反演识别出的所有潜在微观机制,即便其绝对占比较低,也对理解岩石压缩性损伤的初始阶段具有重要意义。图2进一步通过运动模式示意图直观展示了5种裂纹的几何特征,图中双裂纹面及箭头示意了相应的相对运动方式。
材料断裂破坏过程中,张拉裂纹和剪切裂纹所释放的声发射信号明显不同,张拉裂纹释放的纵波能量较大,其声发射波形上升时间短且频率高;而剪切裂纹释放的横波能量较大,其声发射波形上升时间长且频率低。对于张拉裂纹而言,出现“高AFRA”的现象,剪切裂纹则与之相反[11]。其中RAAF的引入物理修正计算公式分别如下:
RA=taexp(αta)fcAkerf(ta2τ)
AF=Ceffβexp(tbτd)γ0tb1+(f0fs)2
式中:ta为精准上升时间,α为介质衰减系数,fc为信号中心频率,A为幅值,k为仪器系统系数,τ为上升时间常数,Ceff为有效振铃计数,β为时间窗口修正系数,tb为能量加权持续时间,τd为能量衰减时间常数,γ0为振铃阈值因子,f0为信号主频率,fs为采样频率。
基于上述定义的RA值与AF值,可构建一种基于声发射波形特征的经验性裂纹分类模型。该模型的核心在于利用RA-AF二维散点图中数据点的分布规律,通过设定合适的分界线,实现对张拉与剪切两类主要裂纹的快速区分。具体分类原则如下:若某微裂纹事件计算所得的AFRA的比值(即AF/RA)大于某一经验阈值K,则判定该事件为张拉裂纹,反映材料中张拉裂纹的萌生或扩展;反之,若AF/RA值小于K,则归类为剪切裂纹,对应于剪切滑移机制的活动。在RA-AF散点图中,该判别界线通常表现为一条斜率为K的直线。
需要着重指出的是,阈值K的取值并非固定,其大小依赖于被测材料的力学特性、内部结构以及传感器的频率响应特性。例如,日本混凝土工程学会标准[20]建议采用夹角θ= 45°作为混凝土材料中张拉与剪切裂纹的常规分界。国际材料与结构研究实验联合会(International Union of Laboratories and Experts in Construction Materials,Systems and Structures,RILEM)的相关技术文件也提供了针对不同材料体系的参考K值范围,在实际应用中需结合具体材料进行验证与标定。图3直观展示了基于声发射波形参数进行RA-AF裂纹分类的典型特征。
总体而言,RA-AF分类法提供了一种操作简便、计算高效的工程实用途径,能够对大量声发射事件进行裂纹类型的快速、初步判别,尤其适用于现场监测与长期数据的初步筛查。
试样制备流程严格遵循国际岩石力学学会(International Society for Rock Mechanics,ISRM)推荐规范,以确保试样的均一性及试验数据的可靠性。首先,从一块完整、无宏观裂隙的砂岩岩块中钻取岩芯并切割;随后采用双端面磨平机进行精密加工,保证试样两矩形端面的不平行度小于0.02 mm,以最大限度减小端部约束引起的应力集中效应。最终加工获得尺寸为30 mm×100 mm×100 mm(x×y×z)的立方体试样,从15块备选岩样中选取3块尺寸公差在±0.1 mm以内的代表性试样,依次编号为SY–1,SY–2和SY–3。
为精确表征试样内部微裂纹的时空演化规律,在每块试样前后2个y-z主平面上各布置4个声发射传感器,共8个传感器(编号1~8),构成对称阵列以保障空间定位精度。传感器安装位置距试样边缘20 mm,接触面均匀涂抹凡士林作为耦合剂,并采用磁性夹具固定,确保声学耦合一致、信号可靠。
试验加载采用TYJ–500 kN微机控制电液伺服岩石剪切流变试验系统,沿y轴方向(高度方向)进行单轴加载,加载面为30 mm×100 mm[21]。该设计充分利用试样2个y-z平面(100 mm×100 mm)作为传感器布置面,优化阵列结构,以提升裂纹动态捕捉能力。
完整的试验系统包括试样准备、传感器布置与断铅试验、加载系统与声发射采集系统4个主要部分,其工作流程如图4所示。该系统实现了从试样制备、传感器标定到加载测试与数据采集的全流程精准控制。
在正式加载开始前,需进行断铅试验以验证声发射系统的定位精度并计算传感器耦合系数Cs。断铅操作时,铅笔与试件表面保持45°夹角,断铅后,系统记录8个传感器接收到的P波初动振幅A(x),并利用已知断铅点位置进行事件定位。
结果表明,系统定位结果与实际断铅点位置高度吻合,验证了定位系统的准确性。断铅试验后,当试样中产生声发射事件或裂纹面时,每个传感器都会得到一个声发射波的入射方向向量ri以及传感器所在界面的法向量ti,裂纹面到每个传感器的距离为Ri(i = 1,2,…,8),通过式(3)计算这8个传感器的对应的反射系数Re(tr),再通过式(4)计算并记录这8个传感器的耦合系数Cs,以便于后续矩张量反演计算。
试样安装时,其上、下端面与试验机压板之间涂抹一定量的凡士林作为润滑剂,完成传感器耦合系数标定与系统定位验证后,将试样安置于压力机加载平台上。3块试样分别采用3种不同的单轴压缩加载路径,具体方案如下:
SY–1试样采用单调位移控制加载,加载速率设定为0.002 mm/s,连续加载直至试样发生宏观破坏;
SY–2试样采用循环加卸载方式,以负荷控制进行,加、卸载速率均为1 kN/s。加载流程为:从0 kN开始加载至10 kN后,卸载至0 kN;继而加载至20 kN,再卸载至10 kN;随后加载至30 kN,卸载至20 kN,…如此阶梯式递增循环,直至试样破坏,且每一循环均从当前峰值荷载开始卸载;
SY–3试样采用分级循环加卸载,同样以负荷控制,加、卸载速率保持1 kN/s。其加载程序分为多个应力水平阶段,依据SY–1试样的峰值应力σc(对应荷载为197 kN,研究中取整为200 kN进行阶段划分)设定每级荷载:第一级为加载至25%σc (50 kN)后卸载至15%σc(30 kN),重复5次;第二级为加载至50%σc(100 kN)后卸载至40%σc(80 kN),重复5次;第三级为加载至75%σc(150 kN)后卸载至65%σc(130 kN),重复5次,…,后续每级荷载水平提高,并保持卸载幅值为10%σc(即20 kN),每级均进行5次循环,直至试样破坏。
3块试样按编号顺序(SY–1,SY–2,SY–3)依次进行加载测试。加载启动后,待压力机上压板与试样上表面均匀接触,随即开始施加轴向荷载。同时启动声发射采集系统,随着轴向荷载的增加,岩石内部微裂纹开始萌生、扩展并释放弹性波,声发射系统探测到的撞击数量随之显著增多。这些撞击在声发射监控软件的显示屏上实时显现为一连串动态增加的点,每一个点精确对应一个被有效触发的声发射撞击,并记录下该撞击所产生的一个完整波形数据。加载过程持续进行,声发射撞击点的密集程度直观反映了试样内部损伤累积的速率和强度。最终,当试样达到承载极限时,发生宏观破坏。在整个加载的全过程中,声发射系统始终保持连续工作状态,无间断地记录下所有被触发的声发射撞击及其对应的完整波形数据,为后续的矩张量反演和RA-AF分析提供完备的数据基础。
声发射技术可有效捕捉岩石在受力过程中内部微裂纹萌生、扩展和贯通过程所释放的弹性波信号,其特征参数(如幅值、振铃计数、上升时间、持续时间和能量等)是揭示岩石损伤演化规律的重要依据。为初步探究3块试样在单轴压缩破坏过程中的声发射行为特性,本文选取能够直接反映裂纹扩展强度的幅值参数,并结合荷载–时间曲线进行综合分析。
岩石在受载变形过程中,内部微裂纹的闭合、滑移或张拉等行为均会诱发声发射事件,并伴随能量释放[9]图5展示了SY–1,SY–2和SY–3这3块试样在加载过程中荷载与声发射幅值随时间的变化关系。
对于SY–1试样,随着荷载持续增大,声发射信号的整体幅值水平呈现出逐渐上升的趋势,较高幅值(>80 dB)的声发射事件数量也有所增加,反映出材料内部损伤随应力水平的提高而持续累积。在接近峰值应力时,可观察到声发射活动显著增强,较高幅值事件的出现更为密集,暗示宏观破裂进入临界阶段;最终试样在峰值应力附近发生破坏,并伴随出现幅值较高的声发射信号,显示出主破裂的瞬态贯通特征。
对于SY–2试样,在每个加卸载循环中,声发射信号的幅值变化与外力变化呈现一定的相关性,表现为加载过程中声发射响应增强,而卸载阶段信号幅值总体较低,显示出能量释放与应力状态改变之间的关联。
对于SY–3试样,其声发射行为在不同应力水平的多循环加载中表现出一定阶段性。在固定应力水平多次循环中,声发射幅值的变化与荷载变化具有一致性;而随着荷载水平的逐级提升,声发射信号的幅值整体呈现递增趋势,表明每级应力历史均诱导了新的损伤机制;最终在试样临近破坏时,声发射幅值达到较高水平,显示出整体失稳的前兆特征。
RA-AF参数提取与分类的步骤如下:
(1)导出记录的声发射撞击数据(txt格式)。
(2)针对每个数据,精确提取4个关键参数:上升时间、幅值、持续时间和振铃计数,注意,这里的幅值单位为dB,需要转化成以伏特(V)为单位,转换公式如下:
dBAE=20lg(Vmax1μvolt)C
式中:dBAE为幅值;1μvolt表示1 μV,即10-6 V;C为前放增益,即前置放大器的分贝增益值。
解出Vmax计算式为
Vmax=10dBAE+C206
(3)根据式(8)和(9)计算每个撞击的RAAF值。
(4)将所有撞击的RAAF值绘制在二维散点图上。根据标准[20],剪切裂纹与张拉裂纹的经验阈值斜率K = tan45°= 1。据此将事件进行分类,AF/RA>1为张拉裂纹;AF/RA<1为剪切裂纹。
(5)鉴于撞击数量庞大,为更直观地展示分布密度,将二维散点图转换为二维核密度图,以颜色深浅反映撞击聚集程度。
试验结束后,导出所有记录的声发射事件数据,包含每个事件的发生时间、幅值、能量、持续时间等参数及对应波形文件。矩张量反演处理的核心步骤如下[22]
(1)P波初动拾取:对于每个声发射事件对应的6~8个传感器,精确提取P波初动振幅值。
(2)构建方程组:对于每个声发射事件,利用P波初动振幅值,结合式(2)以及传感器坐标、声发射事件坐标等信息,构建一个包含6个独立未知矩张量分量的超定方程组。
(3)矩张量求解:采用最小二乘法求解上述超定方程组,得到该声发射事件对应的整个矩张量矩阵M
(4)裂纹类型分类:对求解得到的每个矩张量M进行特征值分解,计算法向量n、运动向量l和张拉角γ,并根据γ值范围将每个微裂纹事件定量分类为剪切、拉剪混合、压剪混合、张拉和压缩5种类型。
为高效实现上述反演计算和分类,本文开发了基于MATLAB GUI的专用批处理程序。该程序通过读取声发射事件(源)坐标、传感器坐标及P波初动振幅等数据文件,自动执行矩张量求解、特征值分解及张拉角计算,并最终输出每个事件的裂纹类型分类结果。设置完成后,程序可自动完成全部数据的批处理计算,能够获得试样在加载全过程中微裂纹事件的时空分布及5种裂纹类型的演化结果。
本文中所称“微裂纹”,指由声发射事件所表征的、尺度通常在毫米级以下的微观损伤;而“宏观裂纹”指在试样破坏时形成的、尺度在毫米级以上且肉眼可见的主控裂纹面。微观裂纹贯通形成宏观裂纹是一个从量变到质变的非线性演化过程,该过程与岩石材料的非均质性、应力状态及能量积累密切相关。为客观识别这一损伤局部化临界状态,本文提出以下定量判据:当某一局部区域(如1 mm3的立方体单元)内的微裂纹数量在单位应力增量下出现突增,即微裂纹数量–应力曲线斜率显著增大,且在空间分布上形成连续、高密度的簇集区时,即认为微观损伤开始贯通并逐渐形成宏观裂纹。
基于矩张量反演程序的计算结果,系统获得了3块试样在加载全过程中微裂纹的时空演化特征。图68分别展示了SY–1,SY–2和SY–3试样在不同应力水平下的微裂纹空间分布及演变。图中每个标记点代表一个声发射事件(即一个微裂纹),其颜色对应于5种裂纹类型,绿色表示剪切裂纹,蓝色表示拉剪混合裂纹,黄色表示压剪混合裂纹,紫色表示张拉裂纹,红色表示压缩裂纹。对于SY–1和SY–2试样,选取20% σ,40% σ,60% σ,80% σ和100% σ(峰值应力)5个特征应力水平进行统计;而对于采用分级循环加卸载的SY–3试样,则选取了每一级的5次循环结束点及峰值应力状态进行统计。
图68可知,随着轴向应力的增加,微裂纹密度持续增加,事件空间聚集性增强,裂纹间相互作用加剧。至临近宏观破坏阶段(80% σ以上),微裂纹网络加速贯通并最终形成宏观破裂带。值得注意的是,该破裂带的空间形态与试样实际破坏面(见图6(f),7(f),8(e))吻合,直观揭示了细观损伤累积、局部化直至宏观贯通的完整物理过程,印证了矩张量反演技术捕捉裂纹演化时空规律的有效性。
图910分别给出了3块试样在单轴压缩过程中的完整应力–应变曲线以及微裂纹数量随应变变化的关系曲线。由图9可知,SY–1试样应力–应变曲线呈现出典型的岩石单轴压缩破坏全过程,包括初始压密阶段、弹性变形阶段、塑性稳定扩展阶段和峰后破坏阶段。其峰值应力最高,表明在该加载方式下材料展现出最大的承载能力;SY–2试样应力–应变曲线中每一次加卸载循环均形成一个滞回环,反映出岩石在反复荷载作用下的能量耗散与不可逆变形累积,其最终峰值应力低于SY–1试样;SY–3试样应力–应变曲线在每一荷载水平下进行5次循环,因此形成多个密集的、应力水平逐级抬升的滞回环簇。该方法更清晰地揭示了岩石在不同应力阈值下的变形记忆效应和疲劳损伤行为,其峰值应力介于SY–1试样与SY–2试样之间。
图10可知,3块试样的微裂纹数量均随轴向应变的增加呈上升趋势,表明在单轴压缩条件下砂岩损伤以不可逆方式持续累积。其中,SY–1试样微裂纹总数最多,且在峰值应变附近曲线斜率显著增大,反映出临近破坏前裂纹扩展加速、相互贯通并趋于失稳的灾变性特征;SY–2试样总裂纹数量最少,其曲线呈阶梯式缓慢上升,后期才出现明显增长,显示出循环加卸载方式对裂纹扩展的阶段性抑制;SY–3试样总裂纹数介于二者之间,曲线在每一荷载水平的多循环内仅小幅增长,表明该加载路径下损伤以渐进方式逐步积累。不同加载路径显著影响微裂纹的演化进程与最终破坏模式。
综合图910可知,峰值应力与累积微裂纹数量呈正相关关系,即宏观力学强度越高,其内部损伤演化也越充分。单调加载试样(SY–1)凭借持续的荷载作用促使裂纹充分扩展,因此微裂纹数量最多、峰值应力最高;而采用循环加卸载路径的试样(SY–2,SY–3)因中间存在卸载阶段,一定程度上延缓了裂纹的持续发展,导致总裂纹数量减少,峰值应力也相应降低。这一结果从微观–宏观关联角度表明,岩石的力学行为不仅取决于最终破坏状态,也与损伤累积的路径紧密相关。
为系统揭示砂岩在单轴压缩下微裂纹类型及演化规律,本文基于矩张量反演结果对全部声发射事件进行了裂纹类型的定量统计。图11通过饼图直观展示了3块试样在整个破坏过程中5种裂纹类型的数量占比分布,从而从微观尺度定量解析砂岩的破裂特征。
统计结果表明,3块试样在整个破坏过程中产生的裂纹类型均以剪切裂纹为主导。其中,SY–1试样剪切裂纹占比62.83%,SY–2试样占比58.58%,SY–3试样占比67.74%。拉剪混合与压剪混合裂纹次之,张拉裂纹和压缩裂纹占比最低。这一结果从微观尺度定量证实了砂岩在单轴压缩条件下的宏观剪切破坏本质。
图12进一步展示了3块试样中5种裂纹类型随应力水平演化的分布情况。为便于拟合分析,将SY–3试样的横轴设为1,2,3,4,分别代表第1,2,3级循环及峰值应力4个应力水平阶段。所有拟合曲线均符合y = Ax3Bx2CxD函数关系。
图12可知,所有类型的微裂纹数量均随轴向应力增加而呈现上升趋势。其中,剪切裂纹数量的增长最为显著,成为驱动试样最终失稳贯通的直接动力源。虽然张拉裂纹、压缩裂纹、拉剪混合裂纹和压剪混合裂纹事件的绝对数量相对较少,但其演化趋势与剪切裂纹基本同步,均表现为随应力升高而缓慢增加。这表明在砂岩的渐进破坏过程中,尽管剪切破坏占据主导地位,但其他类型的微裂纹(特别是混合裂纹)也参与了损伤的累积与扩展,共同构成了复杂的细观破坏协同网络。
为验证矩张量反演结果的可靠性并探究其与RA-AF方法的一致性,本文对声发射信号进行了RA-AF参数分析。通过计算得到每个声发射信号的RAAF值,并绘制了如图13所示的二维核密度分布图。图中颜色梯度从紫色变化至红色,分别对应最高与最低的概率密度,直观反映了数据的分布集中程度,其量纲为坐标轴单位乘积的倒数,具体单位为ms-1·V·kHz-1。依据标准[20],将声发射信号特征划分为张拉裂纹与剪切裂纹两类。
统计结果显示,3块试样中被归类为剪切裂纹的撞击数占比均大于50%,其中SY–1试样剪切裂纹占比55.92%,SY–2试样剪切裂纹占比59.84%,SY–3试样剪切裂纹占比64.95%。这一结果与前述矩张量反演得出的“剪切裂纹主导”的结论一致,特别地,SY–3试样的剪切裂纹占比最高,暗示了复杂加载路径更易诱发剪切裂纹损伤累积。RA-AF与矩张量分类在纯剪切裂纹事件上表现出良好的一致性,但在其他裂纹类型的判别上存在一定差异。这种差异提示了经验参数法与理论反演法在复杂破裂机制解析中的互补性。
为深入揭示RA-AF参数差异的物理根源,本文对典型破裂事件的声发射信号进行了从时域到频域的系统性分析。首先,采用小波阈值去噪法对原始信号进行预处理,以抑制环境噪声并保留信号特征,得到纯净的去噪后信号,小波阈值去噪主要包含3个核心步骤:首先,通过小波变换将原始信号分解至小波域,得到其小波系数;其次,根据噪声特性设定合适的阈值,并对低于该阈值的小波系数进行剔除或收缩处理;最后,基于处理后保留的小波系数进行小波逆变换,重构出去噪后的信号[23]。同时,对其进行快速傅里叶变换(fast fourier transform,FFT)得到能量谱密度(ESD)图,以分析信号能量的整体频率分布,ESD的计算公式如下:
ESD(f)=|s(t)e2πiftdt|2
式中:s(t)为去噪后的时域声发射信号,是时间t的函数,单位通常为伏特(V);f为频率;i为虚数单位;e−2πift为傅里叶变换的核函数,用于将时域信号s(t)分解为不同频率的正弦和余弦分量。
对其进行短时傅里叶变换(short-time fourier transform,STFT)得到二维频谱图(时频谱),以观测频率成分随时间的变化。STFT结果是一个关于时间t0和频率f的二维函数STFT(t0f)。其计算公式如下:
STFT(t0f)=s(τ)w(τt0)e2πifτdτ
式中:τ为积分时间变量,在积分过程中遍历整个时间轴;t0为中心时间,表示当前分析的时间点,即窗函数的中心位置;w(τt0)为窗函数(如汉宁窗、汉明窗),其中心位于时间t0处,该函数在t0附近有较高的权重,而离t0较远时权重衰减至0。
整个处理流程构成了一个完整的分析链条,如图14所示,旨在从物理机制上合理解释RA-AF参数的分异性,并为矩张量反演的分类结果提供波形层面的佐证。图1516分别展示了典型的张拉裂纹和剪切裂纹产生的波形图及其频谱分析结果。
结果表明,张拉与剪切裂纹所辐射的声发射信号在时域和频域上展现出本质不同的特征模式,这直接反映了其不同的破裂动力学过程。张拉裂纹信号表现为典型的突发型波形,其核心特征为极短的上升时间(约0.6 μs)、较高的幅值以及短暂的持续时间。在频域上,其能量高度集中于以约96 kHz为中心的狭窄高频波段,表现为尖锐的频谱峰,尽管瞬时幅值可达0.108 V,但总能量密度相对有限。该特征源于法向张应力主导下裂纹面的瞬时、快速分离,其突发性破裂过程高效地激发出高频弹性波。与之形成鲜明对比的是,剪切裂纹信号呈现为显著的振荡型波形,其特征是漫长的上升时间(约442 μs)、较低的幅值以及持久的持续时间。其频谱能量则广泛分布于较宽的中低频范围,峰值频率约94 kHz,虽然频谱形态宽缓,但其持续振荡特性使得信号幅值可达0.226 V,导致整体能量密度显著高于张拉裂纹。此特征根源于切向应力主导下裂纹面间的摩擦滑动过程,该过程能量释放缓慢、持续,导致频带展宽。深入的波形分析不仅合理解释了剪切裂纹对应高RA值(上升时间长)、张拉裂纹对应高AF值(主频偏高)的物理成因,更从震源物理机制层面为矩张量反演对裂纹类型的精细分类提供了独立的、强有力的波形证据支撑。
本文通过融合矩张量反演与RA-AF分析,系统揭示了砂岩在单轴压缩条件下微裂纹演化的细观机制与破裂主导模式。上述结果表明,砂岩在单轴压缩下以剪切破坏为主导,且不同类型裂纹在演化中表现出明显的协同性与应力路径依赖性。以下结合现有研究进展与本文的发现,对关键问题作进一步讨论。
矩张量反演与RA-AF分析分别基于物理机制与经验信号特征,本文结果显示两者在裂纹类型的宏观判断上具有较好一致性,尤其在判别剪切主导模式方面表现出了较强的相互验证能力。然而,RA-AF方法在处理混合模式裂纹时显示出明显局限性,其分类结果依赖于经验阈值K的选取,且难以有效识别拉剪、压剪等复合裂纹。这一局限在复杂应力路径或非均质岩体中更为突出。尽管矩张量反演方法可提供更精细的裂纹类型分类,但其对传感器布置、波速标定、信号信噪比的依赖性较强,在实际工程监测中实施难度较大。因此,未来研究可致力于开发融合两类方法优势的混合诊断模型,如基于机器学习的RA-AF动态阈值识别方法,或引入多参数联合判据,以提升复杂环境下裂纹类型识别的稳健性。
本文引入单调加载、循环加卸载及分级循环加卸载3种应力路径,重点探讨了扰动应力(如循环加卸载引起的应力波动)对损伤演化的影响。结果表明,应力路径显著控制着微裂纹类型、数量与空间演化进程。与单调加载(SY–1试样)相比,经历扰动应力作用的SY–2和SY–3试样,其总微裂纹数量较少,且剪切裂纹占比在复杂路径(SY–3试样)下最高。这揭示了一个关键机制:扰动应力通过促进裂纹面的反复闭合与摩擦滑移,不断优化和强化剪切优势路径,从而抑制了其他类型裂纹的扩展,加速了损伤的局部化进程。这一发现强调,对岩体稳定性的评估必须考虑其应力历史,特别是工程活动(如开挖、爆破)引发的扰动应力场,其对损伤累积的加速效应不容忽视。
本文的一个重要进展在于,从震源物理机制上统一了矩张量分类与RA-AF参数差异。波形分析表明,张拉裂纹的突发特性与高频能量集中特征,源于法向应力的突然释放;而剪切裂纹的振荡特性与宽带能量分布特征,则与切向摩擦滑移的持续能量耗散过程相对应。这不仅为RA-AF经验分类提供了坚实的物理学解释,也印证了矩张量反演结果的合理性。不同应力路径下裂纹类型比例的差异性,进一步表明破裂机制是局部应力状态与材料微观结构共同作用的结果,这为发展考虑应力路径效应的损伤本构模型提供了重要的细观试验依据。
本文研究仍存在一定局限性。首先,试样尺寸相对较小,且试验在单轴压缩条件下进行,未能考虑围压与工程尺度效应对破裂机制的影响;其次,RA-AF分析中所用经验阈值K的取值仍依赖现有标准,未针对砂岩材料进行特异性标定。未来工作可在以下方面进一步拓展:开展真三轴加载试验,研究围压与中间主应力对裂纹类型演化的影响;发展基于声发射全波形信息的深度学习裂纹分类方法,减少对经验阈值的依赖;结合CT扫描与数字图像相关(digital image correlation,DIC)技术,实现宏–细观变形的同步观测与破裂机制验证。
本文基于声发射矩张量反演与RA-AF参数分析,系统开展了砂岩在单轴压缩条件下微裂纹演化规律的试验研究,主要得出以下结论:
(1)矩张量反演揭示了砂岩破裂的剪切主导机制。在不同加载路径下,剪切裂纹占比均超过50%,且在临近宏观破坏阶段呈现加速增长趋势,是试样失稳贯通的直接动力源。拉剪混合、压剪混合、张拉与压缩裂纹虽占比较低,但其微裂纹数量与应力水平呈正相关,共同构成了砂岩渐进破坏的细观损伤网络,体现了多类型裂纹的协同演化特征。
(2)微裂纹空间演化呈现了损伤累积至贯通的全过程。随着应力的增加,微裂纹萌生并聚集,最终形成与宏观破坏面吻合的破裂带。不同加载路径显著影响微裂纹的数量与演化速率,单调加载(SY–1试样)促使裂纹充分扩展,累积数量最多;循环加卸载(SY–2和SY–3试样)通过应力扰动抑制裂纹持续发展,总裂纹数减少。
(3)RA-AF分析在剪切裂纹主导性判定方面与矩张量结果一致,验证了其作为工程现场快速判断工具的有效性。波形物理机制分析表明,RA-AF参数差异源于破裂动力学的本质区别,张拉裂纹表现为突发型信号,上升时间短、能量集中于高频;剪切裂纹表现为振荡型信号,上升时间长、能量分布宽频,合理解释了经验分类的物理基础。
(4)矩张量反演与RA-AF方法具有明显的互补性。矩张量反演适用于机制驱动的精细分类与时空演化追踪,在识别混合裂纹上具有优势;而RA-AF方法操作简便、计算高效,适用于主要裂纹类型的快速识别。二者结合可形成“快速筛查–精细验证”的分级分析策略,为岩体工程稳定性评价与灾害预警提供更为完备的理论依据和技术支撑。
  • 国家科技重大专项资助项目(2025ZD1011306)
  • 黑龙江省自然科学基金资助优青项目(YQ2023E039)
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2026年第45卷第2期
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doi: 10.3724/1000-6915.jrme.2025.0471
  • 接收时间:2025-07-03
  • 首发时间:2026-06-18
  • 出版时间:2026-02-01
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  • 收稿日期:2025-07-03
  • 修回日期:2025-10-09
基金
National Science and Technology Major Project(2025ZD1011306)
国家科技重大专项资助项目(2025ZD1011306)
Heilongjiang Provincial Natural Science Foundation of China(YQ2023E039)
黑龙江省自然科学基金资助优青项目(YQ2023E039)
作者信息
    1.黑龙江科技大学 矿业工程学院,黑龙江 哈尔滨 150022
    2.黑龙江科技大学 黑龙江省煤矿深部开采地压控制与瓦斯治理重点实验室,黑龙江 哈尔滨 150022

通讯作者:

* 刘刚(1986–),现任副教授,主要从事矿山岩石力学方面的研究工作。E-mail:
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/yslxygcxb/CN/10.3724/1000-6915.jrme.2025.0471
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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