Mechanism analysis on anisotropic degradation of sandstone in freeze thaw environment based acoustic emission
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摘要:
在冻融环境下,层理岩石的破裂对寒区工程稳定性存在一定的威胁,容易发生工程事故。将平行、垂直层理砂岩在0,20,40,60次冻融循环处理后,进行了单轴压缩和声发射试验,研究了不同层理砂岩的力学性质和岩石变形破坏过程中声发射特征参数的变化情况。结果表明:随着冻融作用的加强,两组试样的纵波速度都逐渐变小,孔隙率逐渐变大;平行组试样的抗压强度下降比例和应变增大比例都大于垂直组试样,说明平行组试样的劣化程度高于垂直组试样;两组不同层理砂岩在冻融循环作用下的声发射振铃计数演化曲线趋势变化一致,可分为三个阶段:平静阶段、阶梯式增长阶段、骤增阶段。不同层理方向的砂岩单轴破坏模式不同。平行组试样声发射b值变化呈倒“V”型,而垂直组试样声发射b值变化呈正“V”型,基于RA-AF值变化特征表明平行层理黄砂岩基本以拉伸破坏为主,而垂直层理砂岩在未处理时以拉伸破坏和剪切破坏为主,在60次冻融循环处理后,也以拉伸破坏为主。研究成果为寒区岩土工程的安全评估提供科学依据。
Abstract:In the freeze-thaw environments, the fracturing of bedding rock poses a significant threat to the stability of the projects in cold regions, leading to potential engineering accidents. In this paper, uniaxial compression and acoustic emission tests were carried out on parallel and vertical stratified sandstones after 0, 20, 40, and 60 freeze-thaw cycles. The mechanical properties of different stratified sandstones and the changes in acoustic emission characteristic parameters during rock deformation and failure were studied. The results show that with the intensification of freeze-thaw effects, both longitudinal wave velocities of the two sample groups gradually decrease, and the porosity increases gradually. The proportion of compressive strength decrease and strain increase of the parallel group of samples is greater than that of the vertical group, indicating a higher level of degradation in parallel group. The trend of acoustic emission ringing count evolution curve of the two groups of different stratified sandstones under freeze-thaw cycles is consistent and can be divided into three stages: a calm stage, a stepwise growth stage, and a sudden growth stage. Uniaxial failure modes of sandstones with different bedding directions are different. The change in acoustic emission b-values for parallel group samples presents an inverted “V” shape, while for vertical group samples, it shows a normal "V" shape. Based on the various characteristics of RA-AF values, it is suggested that the parallel stratified yellow sandstone primarily undergoes tensile failure, while the vertical stratified sandstone is mainly dominated by both tensile and shear failures in the untreated condition, and also mainly dominated by tensile failure after 60 freeze-thaw cycles. The research results provide a scientific basis for the safety assessment of geotechnical engineering in cold regions.
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Keywords:
- stratified sandstone /
- freeze-thaw cycles /
- acoustic emission /
- failure mechanicsm
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0. 引言
地质灾害易发性评价是地质灾害风险评价的核心工作内容之一,是通过分析地质灾害位置与其相关因素之间的关系。利用统计、数据挖掘以及地理信息系统在空间上识别地质灾害易发区域,影响因子选取是易发性评价的前提工作[1 − 3]。因子的正确选择取决于地质灾害的类型、机理、特征、案例区、分析的尺度、数据的可用性和使用的评价模型方法[4 − 6]。地质灾害影响因子可划分为以下几类:(1)地质因子:岩性、断层距离(密度)、工程地质岩组、斜坡结构类型、向斜与背斜构造、褶皱轨迹等;(2)地形因子:坡度、坡高(地形起伏度)、高程、地形曲率(平面曲率、剖面曲率、标准曲率、坡形)、坡长、坡位、沟谷密度、地形表面纹理、地形位置指数(topographic position index,TRI)、地形耐用指数(terrain ruggedness index,TPI)、粗糙度指数、地表切割度等;(3)水文因子:河流距离(密度)、降雨、地形湿度指数(topographic wetness index,TWI)、水动力指数、地下水高度、有效补给率、侵蚀程度、输沙能力指数、河谷深度、流路长度、径流强度指数、河流流量等;(4)地表覆盖因子:土地利用、植被指数(NDVI)、土壤类型、土壤厚度、森林类型、土壤渗透率、地表太阳辐射强度等;(5)人类活动因子:道路距离(密度)、居民距离(密度)等;(6)地震因子:地震烈度、峰值加速度、地震密度、震中距离等[7 − 10]。地形因子与地质因子可以表征主导滑坡发生的背景条件,地表覆盖因子、水文因子、人类活动因子与地震因子则反映附加因素加剧滑坡的可能。在已有的研究中,坡度是地质灾害易发性评价工作中最为常用的因素,岩性、高程、坡向、河流距离和断层距离等也是常用的评价因子,而其它因素的适用性因地制宜[11 − 14]。
对于地质灾害影响因子的选择,目前还没有统一的标准,以往研究工作中地质灾害易发性评价选择的影响因子往往是根据经验选取地质因子、地形因子、水文因子中的部分参数,每项研究选取的因子类型存在一定差异,且因子数量不一致[15 − 19]。可能存在以下问题:①选择因子较少,预测精度不足;②选择因子过多,叠加部分因子后预测精度可能达到峰值,叠加过多因子增加一定工作量。此外,是否在地质灾害易发性评价中叠加的因子数量越多,模型预测精度越高或者上下大幅度波动?易发性评价中是否存在“最优因子数量”这一概念?这些问题值得探讨。
基于以上提出的问题,本文在以往研究工作基础上,以四川省汶川县作为案例区,选取多个常用地质灾害影响因子,将所选取影响因子按照一定排列组合模式运用信息量法进行案例区地质灾害易发性评价,并采用成功率曲线验证叠加不同数量对地质灾害易发性评价精度的影响[20]。
1. 研究方法
本研究选取“5•12”汶川地震后地质灾害频发的汶川县为案例区,案例区共发育有地质灾害690处,包括崩塌192处、滑坡351处、泥石流147处(图1)。基于所收集资料与已有研究基础[16],选取地质灾害易发性评价常用影响因子11种:地貌类因子(高程、坡度、起伏度、坡形、起伏度、沟谷密度)、地质类因子(工程岩组、断裂)、水文类(河流)、人类工程活动(道路、植被指数),并对因子进行分级(表1)。运用信息量模型(表1),以案例区70%历史灾害点为训练样本,计算每个影响因子各个分级的信息量,信息量计算方法如式(1)—(2)所示。根据不同排列组合叠加3到11个因子信息量获得对应的地质灾害易发性指数分布图,易发性指数越高代表地质灾害易发性越高。以30%历史灾害点和非灾害点为检验样本,本文中非灾害点为历史地质灾害点数据1 km缓冲区范围外随机生成的点位数据,运用成功率(receiver operating characteristic,ROC)曲线确定线下面积 (area under curve,AUC)值对各个结果进行预测精度评价,历史灾害点成功率曲线AUC值越趋近于1代表其评价精度越高,而非灾害点成功率曲线AUC值越趋近于0代表其评价精度越高。对比各个组合结果,分析叠加影响因子数量与地质灾害易发性评价精度的关系。研究思路如图2所示。
表 1 因子分级及信息量Table 1. Classification and information value of the factors因子 分级 灾害点比例/% 因子分级
面积比例/%信息量 高程/m [784, 1200) 27.87 2.60 3.42 [1200, 1700) 40.78 8.69 2.23 [1700, 2200) 19.88 13.23 0.59 [2200, 2700) 7.26 16.98 −1.23 [2700, 5832] 4.21 58.50 −3.80 坡度/(°) [0, 10) 19.88 3.21 2.63 [0, 20) 26.85 13.30 1.01 [20, 30) 34.40 32.75 0.07 [30, 40) 14.95 37.66 −1.33 [40, 88] 3.92 13.08 −1.74 地面起伏度/m [0, 200) 15.09 4.49 1.75 [200, 400) 68.36 42.07 0.70 [400, 600) 15.38 45.22 −1.56 [600, 800) 1.02 7.29 −2.84 [800, ∞) 0.15 0.93 −2.69 沟谷密度
/(km·km−2)[0.23, 0.46) 1.30 13.65 −3.39 [0.46, 0.58) 6.08 24.26 −2.00 [0.58, 0.69) 31.84 33.00 −0.05 [0.69, 0.82) 42.26 23.38 0.85 [0.82, 1.23] 18.52 5.71 1.70 道路距离/m [0, 200) 1.01 1.70 −0.75 [200, 400) 2.32 1.68 0.46 [400, 600) 2.32 1.66 0.48 [600, 800) 2.32 1.66 0.48 [800, 1000) 2.32 1.64 0.50 [1000, ∞) 89.73 91.66 −0.03 断层距离/m [0, 500) 28.94 9.96 1.54 [500, 1000) 25.90 8.80 1.56 [1000, 1500) 10.27 7.27 0.50 [1500, 2000) 7.96 6.07 0.39 [2000, ∞) 26.92 67.90 −1.33 工程岩组 硬质岩组 18.38 9.56 0.94 软硬互层岩组 46.74 53.02 −0.18 软质岩组 34.88 37.42 −0.10 河流距离/m [0, 200) 8.10 1.59 2.35 [200, 400) 10.27 1.59 2.69 [400, 600) 11.29 1.59 2.82 [600, 800) 5.79 1.57 1.88 [800, 1000) 3.47 1.57 1.14 [1000, ∞) 61.07 92.07 −0.59 坡向 北 6.34 11.30 −0.83 北东 11.21 12.43 −0.15 东 16.37 14.93 0.13 南东 19.03 13.62 0.48 南 7.96 11.82 −0.57 南西 9.00 12.47 −0.47 西 12.24 11.26 0.12 北西 17.85 12.17 0.55 坡形 凹形坡 68.80 54.79 0.33 凸形坡 31.20 45.21 −0.53 植被指数 [−1, 0) 2.03 4.76 −1.23 [0, 0.1) 18.43 22.12 −0.26 [0.1, 0.25) 31.64 20.93 0.60 [0.25, 0.4) 23.08 23.69 −0.04 [0.4, 0.55) 21.04 18.31 0.20 [0.55, 0.6] 3.77 10.18 −1.43 (1) (2) 式中:I——评价区某单元信息量预测值;
——分布在因素 内特定类别内的灾害点单元数; ——案例区含有灾害点分布的单元总数; ——案例区内含有评价因素 的面积; ——为案例区总面积; ——致灾因子指标值。2. 因子排序组合
首先采用层次分析法确定每个因子的权重,层次分析法是一种多指标分析评价方法,具有精度高,使用方便等特点。通过专家估计两两影响因子之间的关系构造矩阵对所有影响因子进行两两比较确定各个影响因子的权重,这样避免了个别比较不合理而造成的结果偏差过大。
然而层次分析法带有一定的主观性,为避免主观性,选取8位从事工程地质研究工作学者对案例区11个因子进行打分,8位专家打分结果平均值作为因子最终权重值(表2)。最终确定各个因子对地质灾害敏感度从高到低排序为:①断裂②岩性③坡度④河流⑤坡形⑥起伏度⑦沟谷⑧高程⑨公路⑩坡向⑪植被指数。结合汶川县地质灾害发育分布特征及每个因子的信息量综合分析,区内发育汶茂断裂与北川映秀断裂,地质灾害主要集中于河流两岸,受坡度控制明显,且区内地质灾害与构造活动有着高度耦合性,这一结论与已有研究成果是相同的[16]。综上说明通过多位专家打分的汶川县各个因子对地质灾害敏感度排序结果合理性较高。
表 2 因子权重Table 2. Factor weights table专家因子 1 2 3 4 5 6 7 8 平均值 ① 断层 0.055 0.269 0.193 0.223 0.138 0.182 0.209 0.135 0.176 ② 岩性 0.023 0.133 0.182 0.124 0.168 0.106 0.182 0.143 0.133 ③ 高程 0.171 0.053 0.018 0.022 0.099 0.138 0.012 0.056 0.071 ④ 坡度 0.028 0.116 0.108 0.146 0.083 0.203 0.141 0.112 0.117 ⑤ 坡向 0.169 0.014 0.038 0.041 0.086 0.106 0.024 0.023 0.063 ⑥ 沟谷密度 0.063 0.064 0.082 0.055 0.082 0.106 0.096 0.073 0.078 ⑦ 坡形 0.128 0.105 0.046 0.100 0.073 0.046 0.105 0.090 0.087 ⑧ 河流 0.123 0.031 0.084 0.103 0.042 0.043 0.089 0.196 0.089 ⑨ 道路 0.128 0.042 0.078 0.064 0.057 0.021 0.057 0.075 0.065 ⑩ 植被指数 0.044 0.053 0.018 0.043 0.036 0.036 0.050 0.028 0.039 ⑪ 起伏度 0.069 0.119 0.153 0.079 0.138 0.014 0.035 0.067 0.084 为了避免按照某种顺序叠加因子导致结果规律的偶然性,本文将各个因子按照不同排列组合成由3个因子至11个因子组成的评价模型,因子组合分为两类:顺序数组与随机数组。顺序数组涵盖两种组合:因子对地质灾害发生的敏感度从高至低排列与从低至高排列模式;随机数据由编程语言随机函数生成1~11中包含不同个数并且不重复的随机数组。因子排列组合如表3所示。
表 3 因子排列组合Table 3. Factor combination table因子数量 顺序组合 随机组合 组合 1 组合2 组合3 组合4 A(3) ①②③ ⑪⑩⑨ ③⑦⑪ ②④⑥ B(4) ①②③④ ⑪⑩⑨⑧ ①③⑤⑩ ②⑤⑨⑪ C(5) ①②③④⑤ ⑪⑩⑨⑧⑦ ②③⑤⑦⑨ ⑤⑦⑨⑩⑪ D(6) ①②③④⑤⑥ ⑪⑩⑨⑧⑦⑥ ①③④⑤⑧⑩ ②④⑥⑨⑩⑪ E(7) ①②③④⑤⑥⑦ ⑪⑩⑨⑧⑦⑥⑤ ①②④⑥⑦⑧⑪ ①③④⑤⑧⑨⑩ F(8) ①②③④⑤⑥⑦⑧ ⑪⑩⑨⑧⑦⑥⑤④ ①④⑤⑥⑧⑨⑩⑪ ①②③⑤⑦⑨⑩⑪ G(9) ①②③④⑤⑥⑦⑧⑨ ⑪⑩⑨⑧⑦⑥⑤④③ ①②④⑤⑥⑧⑨⑩⑪ ②③④⑥⑦⑧⑨⑩⑪ H(10) ①②③④⑤⑥⑦⑧⑨⑩ ⑪⑩⑨⑧⑦⑥⑤④③② ①②③⑤⑥⑦⑧⑨⑩⑪ ①②③④⑤⑥⑦⑧⑨⑪ I(11) ①②③④⑤⑥⑦⑧⑨⑩⑪ ⑪⑩⑨⑧⑦⑥⑤④③②① ①②③④⑤⑥⑦⑧⑨⑩⑪ ①③④⑤⑥⑦⑧⑨⑩⑪ 3. 评价精度分析
将各个因子信息量按照表3中因子组合方式分别叠加,计算出各个组合的案例区地质灾害易发性指数图(图3),运用成功率曲线验证和比较各个组合模型易发性精度。
如图3所示,为多个组合模型不同数量因子叠加的案例区地质灾害易发性指数图,结果表明当叠加因子数量3~5个时,易发性指数图受单个因子控制性较为明显,例如组合1A(3)与组合1B(4)中断层控制易发性指数图最为明显、组合2A(3)中道路控制易发性指数图最为明显。而当叠加因子6~7个时,地质灾害易发性指数图受单个因子控制性不再明显,显现出了多个因子的叠加效应,但不同组合模型的地质灾害易发性指数图图面信息差异性较大,易发性高分布的区域和面积各不相同。当叠加至8个以上因子后,各个组合模型的易发性指数图相似性较高,显现出的高易发区与实际情况匹配度较高。
对比各类组合模型基于历史地质灾害点验证样本的成功率曲线(图4),统计出随因子数增多成功率曲线下面积(AUC)变化规律(图5)。叠加3个因子预测精度较差,组合2和组合4 中AUC值仅在0.65左右,而组合1和组合3相对于组合2和组合4同等数量因子组合中叠加预测精度较高。其共同规律为:4种组合中AUC值随因子数增多而不断增高,即随着叠加因子数量增多预测精度不断增高,但叠加因子数至8个时,AUC值不再明显上升与下降,其值约为0.9,浮动幅度在0.005左右,说明叠加8个以上因子时预测精度不再变化。
对比各类组合基于非地质灾害点样本的成功率曲线,统计出随因子数增多成功率曲线下面积(AUC)变化规律(图6、图7)。由图中可观察出叠加少于8个因子的组合随叠加因子数增多,AUC值浮动较大,且有着随因子数增多而逐渐下降的趋势。叠加至8个以上因子的组合模型AUC值相对变化浮动较小,稳定于0.385左右。
结合两种地质灾害易发性评价精度检验方法,对比分析了按照不同组合方式叠加3至11个因子的36种组合模型,分析结果发现随叠加因子数量增多,组合模型精度不断提升,但叠加至8个因子后,模型精度不再变化,精度值上下浮动较小,历史灾害点验证样本的AUC值稳定于0.9左右,非灾害点验证样本的AUC值稳定于0.385左右,由于所选用的非地质灾害点为历史地质灾害点数据1 km缓冲区范围外随机生成的点位数据,非地质灾害点又有可能在不久的将来成为新的地质灾害点,AUC难以趋近于0,说明评价模型叠加至8个因子时模型精度已达到峰值,叠加更多因子不会明显提升或降低其精度。
4. 讨论
根据不同因子组合方式可发现,各个因子对于案例区的地质灾害易发性影响存在较大的差异性。由于汶川地区受到2008年“5·12”Ms8.0地震的震裂影响,在断裂带区域地质灾害分布较为密集,断层缓冲区因子对于案例区的地质灾害易发性控制性最强。综合图5和图7中叠加各个因子后AUC值的变化幅度,重新梳理各个影响因子的控制性排序为:断层>河流>道路>岩性>高程>起伏度>坡度>沟谷密度>坡形>坡向>植被指数。这一排序结果与前文通过专家打分确定因子重要性等级排序存在一定差异,分析原因为专家打分存在一定主观性,尽管采用了多个专家打分的平均值,但还是难以消去其主观性。
对比前文四种因子组合模型,它们有着共同的特点,运用验证样本成功率曲线检验各种组合模型评价精度时:模型精度随着叠加因子数增多而提高,叠加至8个因子时模型精度不再变化,趋于平稳状态,AUC稳定于0.9左右,上下浮动约0.005;运用非灾害点样本成功率曲线检验各种组合模型评价精度时:模型AUC值随叠加因子数增多而下降,即模型精度随着叠加因子数增多而上升,同样在叠加至8个因子时模型精度趋于稳定,AUC稳定于0.385。根据这一结果,可以确定当模型选取8个以上因子时,模型精度将不会改变,8个因子可能是地质灾害易发性评价叠加最佳因子数。但这一结论是否正确值得再次证明与讨论。
按照前文多次叠加后确定的因子实际控制性从高到低与从低到高两种组合模型再次检验随着因子叠加数量增多易发性评价精度的变化规律。如图8—9所示,按照因子实际控制性从高到低排列组合,叠加断层、河流、道路3个因子后AUC值已经接近峰值,为0.889,其后再次叠加其它因子,AUC值上下浮动约0.02。而按照实际因子控制性从低到高排列组合,当叠加到最后一个因子(断层)时AUC值才达到峰值。
结合前面的试验研究与后面的验证结果综合分析,造成叠加至8个因子时易发性指数的AUC值最大的原因在于叠加过程中存在一定偶然性:前期四种组合模型在叠加因子时仅靠个人经验或随机组合,未将关键因子优先组合,AUC值无法快速达不到峰值。而当叠加至7~8个因子时已经包含了这类关键因子(例如断层、河流、道路),此时达到了评价结果精度的峰值,其AUC值在0.9上下以0.005浮动。
综上试验研究表明,开展某地区地质灾害易发性评价时,最先开展的工作应是确定出该区域地质灾害的主控因素,例如构造、水文、岩性、地形等因素,即需要开展的是孕灾条件分析。且对于大区域,例如省级地质灾害易发性评价,应根据地质环境条件与地质灾害发育特征,对研究区开展综合分区,找出各个分区的主控因素,进行分区评价。可采用反演分析模式,综合运用信息量模型与ROC曲线法,将逐个因子不同等级所对应的信息量作为检验变量,利用ROC曲线法进行单因子分析,根据AUC值确定各个因子对研究区地质灾害敏感度重要程度排序。
5. 结论
本文以四川省汶川县为案例区,选取广泛应用的11种地质灾害影响因子进行不同排列组合,验证“是否在地质灾害易发性评价中叠加的因子数量越多,模型预测精度越高或者上下波动。”这一问题。经对比试验研究,得出以下结论:
(1)地质灾害影响因子进行随机组合时,叠加因子数量越多,地质灾害易发性评价结果精度越高,但叠加至一定数量因子后评价精度达到峰值,叠加更多因子不会明显提升或降低精度。
(2)地质灾害的发生在不同区域有着不同的主控因子,因子选取原则不仅仅根据个人经验,更应该计算出来每个因子独立的控制性,可采用单因子信息量与ROC曲线组合模型评价结果确定出主控因子,优先叠加控制性较强的因子,能够快速达到易发性评价精度的最高值。
(3)根据本文有限的多次测试结果表明,地质灾害易发性评价中叠加的因子数量越多,模型预测精度越高,叠加过程中如未加入关键因子,模型预测精度将不会达到峰值,说明地质灾害易发性评价存在关键因子,但不存在 “最优因子数量”。
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表 1 试样冻融前后应力、应变峰值变化情况
Table 1 Peak stress and strain changes of samples before and after freeze-thaw
冻融循环
次数应力峰值下降比例/% 应变峰值增加比例/% 平行层理试样 垂直层理试样 平行层理试样 垂直层理试样 20 37.9 13.4 13.4 16.5 40 41.3 29.1 33.7 30.4 60 57.5 52.8 40.7 35.7 -
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