Quantitative assessment for the rockfall hazard in a post-earthquake high rock slope using terrestrial laser scanning

Accepted Manuscript Quantitative assessment for the rockfall hazard in a postearthquake high rock slope using terrestrial laser scanning

Hai-bo Li, Xiao-wen Li, Wan-zhou Li, Shi-lin Zhang, Jia-wen Zhou PII: DOI: Reference:

S0013-7952(17)31883-5 https://doi.org/10.1016/j.enggeo.2018.11.003 ENGEO 4989

To appear in:

Engineering Geology

Received date: Revised date: Accepted date:

29 December 2017 25 October 2018 10 November 2018

Please cite this article as: Hai-bo Li, Xiao-wen Li, Wan-zhou Li, Shi-lin Zhang, Jiawen Zhou , Quantitative assessment for the rockfall hazard in a post-earthquake high rock slope using terrestrial laser scanning. Engeo (2018), https://doi.org/10.1016/ j.enggeo.2018.11.003

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ACCEPTED MANUSCRIPT

Quantitative assessment for the rockfall hazard in a post-earthquake high rock slope using terrestrial laser scanning Hai-bo Lia, Xiao-wen Lib, Wan-zhou Lic, Shi-lin Zhangb, Jia-wen Zhoua,* a

State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University,

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Chengdu 610065, China

College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China

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Sinohydro Bureau 7 CO. LTD., Power Construction Corporation of China, Chengdu

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610081, China

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(*Corresponding author, E-mail address: [email protected])

ACCEPTED MANUSCRIPT Abstract Geometric information and discontinuity characterization of rock masses are the key aspects of the analysis of the evolution and failure mechanisms of rockfalls. With the advantageous use of terrestrial laser scanning (TLS), the accurate three-dimensional (3D) spatial information of rock slopes can be obtained without contact. By conducting a fuzzy

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K-means algorithm in this study, the automatic identification of discontinuity sets is achieved, and dominant occurrences of rock mass discontinuities in each local region can be acquired in

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great detail. This automatic identification method permits the user to visually identify the

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discontinuity sets and acquire their spacial distribution features, e.g. occurrences, spacings, trace lengths and their geometric compounding relationships. At the same time, based on the

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shortest distance (SD) algorithm and the surface-to-plane volume calculation algorithm, the

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distributions, shapes, volumes and scars of the rockfalls can be accurately detected over the monitored time interval. These methods are able to provide adequate investigations and quantitative assessment for the rockfall failure mechanisms and evolutions of the Hongshiyan

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post failure rock slope after the 2014 Ludian earthquake. The topography of the landslide

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surface, mechanical properties, scales and the structural features of the discontinuities have a significant effect on the failure mechanisms, distributions and volumes of rockfalls. The main failure mechanisms of the rockfall investigated in this rock slope can be divided into plane

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failure, wedge failure and toppling failure. The plane and wedge failures mainly occurred in the landslide surface while the toppling failures are mainly observed along the edge of the post failure slope, which is obviously responded by preferred discontinuities. The reverse-dip stratified structure characteristics and the excavation disturbances during the recovering and slope treatment stage induce numerous progressive and continuous failures of rockfalls. The results are beneficial for the design and optimization of rockfall treatments. Keywords: Terrestrial laser scanning; Post failure rock slope; Automatic discontinuity identification; Rockfall detecting; Failure mechanism

ACCEPTED MANUSCRIPT 1. Introduction Rockfalls are the most frequent type of movements on steep slopes such as rock walls in coastal cliffs and the remnant rock scarp after landslides (Dewez et al., 2013). They can affect long stretches of transportation systems, entire villages, construction and mechanical

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equipment, and other human life or property, where these elements at risk are located on or near the base of steep rock slopes (Hantz et al., 2003; Zhou et al., 2013; Gigli, et al., 2014;

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Zhao et al., 2017). Since rockfall is the fastest type of movements, its impact energy (and

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hence the geological hazard) can reach very high values, and it may cause casualties, even if

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the mobilized mass is very small (Abellán et al., 2010; Shen et al., 2017). Rockfalls are mainly controlled by local topography, mechanical properties, orientation

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and distribution of discontinuities (Copons and Vilaplana, 2008; Rowe et al., 2018). However, these parameters are often difficult to obtain by traditional geological surveys, especially for

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high rockwalls or when dealing with active landslides or heavily fractured rock masses. The terrestrial laser scanning (TLS), on the other hand, allows the remote, quick, and accurate

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measurement of all the main geometric characteristics of a rock mass, which provide a new technique for rockfall investigation and assessment (Abellan et al., 2009; Lato et al., 2012;

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Spreafico et al., 2016; Zhou et al., 2017). TLS supplies high-resolution point clouds of the surveyed objects, and with the rapid progress of maximum scanning range and resolution, the TLS technique has become increasingly popular in terrain monitoring and hazard assessment. The primary areas of applications are rockfall and slope instability analysis: for geomorphologic and discontinuities analyses of steep rock slope and coastal cliffs (Armesto et al., 2009; Gigli and Casagli, 2011; Lim et al., 2005), for deformations or displacements monitoring of potential

ACCEPTED MANUSCRIPT unstable rock blocks or landslides (Abellán et al., 2011; Dunning et al., 2009; Kemeny and Post, 2003), and for geohazard assessment (Abellán et al., 2006; Hunter et al., 2003). In regard to this case study, the Hongshiyan Reconstruction Project is one of the first excellent examples of addressing natural disasters by transforming a landslide-dammed lake into a hydraulically engineered lake in China (Lv et al., 2017). The stability of the post failure

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rock slope is crucial to the reconstruction of the project. The slope is high and steep, the rock

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masses are heavily fractured, and the rockfall poses a significant threat to construction

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workers and mechanical equipment at the base of the slope. It is essential to fully investigate the discontinuities and analyze failure mechanisms and evolutions of the rockfalls. Combined

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with field investigations and TLS technologies, the accurate 3D surface models of the Hongshiyan post failure rock slope was established with high-resolution geometry and

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morphology information; the discontinuity sets and their orientations and distributions are visually identified and determined; the distributions, shapes, volumes and scars of the

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rockfalls are accurately detected over the monitored time interval; and the evolutions and failure mechanisms of rockfalls have been quantitatively analyzed and assessed. Finally,

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corresponding comprehensive rockfall treatment measures have been suggested to ensure the long-term safety of the post failure rock slope.

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2. Background 2.1. Overview

On 3 August 2014, an Mw 6.5 earthquake hit the Ludian County, Yunnan Province in Southwestern China (latitude of N27.1° and longitude of E103.3°). The Ludian earthquake has devoured 617 lives and more than 3,000 people were injured, thousands of houses were destroyed and various types of landslides occurred. As shown in Figs. 1a and 1b, the Hongshiyan landslide is the largest landslide in scale induced by the 2014 Ludian Mw 6.5

ACCEPTED MANUSCRIPT earthquake, located on a bank of the Niulan River in Ludian County, approximately 15 km far away from the epicenter (Zhou et al., 2016). Approximately 12 million m3 of the ensuing accumulation of mass waste slid into the Niulan River, blocked the river channel and formed a large V-shaped landslide dam, as shown in Fig. 1c. The landslide-dammed lake that formed is estimated to have a lake capacity of 2.6 × 108 m3 , a catchment area of 11,800 km2 , and a

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maximum length of the backwater area of 25 km (Shi et al., 2017).

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Since the narrow valley topography provides insufficient space to address rock fill

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materials, it is very difficult and expensive to excavate and dredge the landslide dam. On the contrary, there are favorable conditions and unique advantages for the Hongshiyan

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landslide-dammed lake to be converted into a hydraulic engineering project. An alternative processing method is being implemented to reinforce the landslide dam and change it into a

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permanent dam. The stability of the post-earthquake rock slope is the key point during the reinforcement and later operation of the project. The rockfall instability phenomena pose a

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significant threat to the construction workers and mechanical equipme nt at the base of the

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rock slope. 2.2. Geological conditions

The Hongshiyan post-earthquake slope is located in a typical deeply incised narrow valley.

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As shown in Fig. 1b, the original Hongshiyan Mountain was located at a bend in the Niulan River and had a height of approximately 760 m above the riverbed. It was a three-sided hanging mountain with a steep slope of 54º-61º before the earthquake (Fig. 1c). The adverse terrain and the steep bulge shapes had a significant seismic amplification effect on Hongshiyan Mountain (Sepúlveda et al., 2005; Gatmiri et al., 2008). The propagation of seismic waves and its complex interaction with slope rock masses has induced extensive damage and large steep tension fractures, which culminated in a large landslide (Li et al, 2018). As shown in Fig. 2a, after the landslide induced by the 2014 Ludian Mw 6.5

ACCEPTED MANUSCRIPT earthquake, the topography of Hongshiyan Mountain had been significantly changed. The post failure rock slope resembles a large chair shape (Fig. 1c). A tilting platform with the strata gently dipping toward the river and downstream side dividing the slope into two parts; the upper part of the slope is a huge remnant scarp with a height of 350 m and a total width of approximately 900 m along the river; the lower part is an original cliff with a height of 120

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m.

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The Hongshiyan Mountain presents a typical anti-dip stratified structure. As shown in Figs.

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2b and 2c, the main strata consist of three layers from top to bottom: the lower Permian (P) with massive limestone and dolomite, the middle Devonian (D) with sandstone and shale or

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mudstone, and the middle Ordovician (O) with dolomite or limestone. The rock strata mainly dip toward the mountain and downstream side, with a strike of N20°-60°E, dip direction of

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NW and dip angle of 10°-30°. Under the long-term geological structures coupled with the effect of gravity, the rock strata present low-angled folds or flexural deformations. There is

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no regional fault passing through the rock slope area; only one medium fault, F5 (with a strike of N5º-15ºW, dip direction of SW and dip angle of 40°-50°), has developed in the middle of

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the slope. F5 is a control fault that extends along approximately the whole slope with a fault zone typically 0.5-1.0 m in thickness, and it mainly consists of fragmented rocks and fault

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gouges (Fig. 2a).

The Hongshiyan landslide is a typical earthquake- induced failure. The rock mass of the post failure slope is highly damaged because of the seismic loads and the long-term effect of unloading in geologic time. As shown in Figs. 2a and 2d, numerous cracks are developed in the rock slope, dominated by joint set J1 , joint set J2 , and joint set J3 . Joint set J1 , is parallel to the rock strata with a strike of N20º-60ºE, dip direction of NW and dip angle of 10º-30º. Joint set J2 , is widely distributed in the nearly east-west directions with steep dip angles of 78º-83º. The majority joints are opened, rough and mainly parallel to the landslide surface. Joint set J3

ACCEPTED MANUSCRIPT is approximately vertical to the river direction with a strike of N30ºW, dip direction of NE and dip angle of 80º. 2.3. Rockfall threat Due to the favorable geological conditions (up- inclined rock striates) and the post treatment measures (such as slope cutting above an elevation of 1,765 m and active support

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systems in 2016), the rock slope is stable overall. While the rockfall problem is prominent

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under unfavorable conditions, such as rainstorms and aftershocks, because of the densely

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developed joint sets (J1 , J2 and J3 ) and block bodies combine under these factors. As shown in Fig. 1c and Fig. 2a, the landslide surface is large, steep and rough with a

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significant tension failure characteristic. Loose body and local potential unstable rock blocks cutted by multiple interlaced tension and structural planes are widely distributed especially in

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the landslide surface or along the edge of the post failure slope. Rockfall events occur from time to time and pose a significant threat to construction workers and mechanical equipment

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at the base of the slope (Fig. 3). It occurs mostly in the rainy season (from July to September)

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with volumes varying from less than 0.1 m3 to more than 100 m3 . At the same time, project disturbances may induce new occurrences of rockfall from time to time (Fig. 3d). The rockfall risks are hidden and uncertain; despite a series of engineering treatment measures, in

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the rainy season, suspensions are unavoidable to prevent geological disaster.

3. Methods

3.1. Terrestrial Laser Scanner Terrestrial laser scanning (TLS) is known as a ground-based LIDAR (Light Detection and Ranging) system that is based on reflectorless and contactless acquisition and is used to rapidly obtain accurate 3D geometric information of the surrounding scene. The pulse-based scanner transmits laser pulses, emits the reflected signals by the object, and utilizes the

ACCEPTED MANUSCRIPT time-of- flight (TOF) technology to determine the distances between the instrument and points on the reflective surface of the object. It captures the 3D positions of millions of points in the survey area to create a geometrically correct 3D “image” of the survey objects. For each point, the x, y, and z coordinates in the Cartesian coordinate (X, Y, Z) setting at the center of the TLS instrument are collected. In the same time, the optical power of the backscattered echo of the

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emitted signals in each point is record. These values are supplied as so-called reflection

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intensity (i) (Pfeifer et al., 2007). The acquired spatial points, which are referred to as the

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point cloud, can then be used to create accurate 3D surface models or digital elevation model (DEM), for mapping purposes, engineering surveying or further geotechnical analysis.

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Another important application of TLS is for sequential change surveys of objects by comparing successive surface models and calculating difference models for specific periods.

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This procedure provides visualization of objects to temporal surface changes such as deformation, erosion and rockfall, and therefore permits monitoring and quantification of

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changes (Kuhn and Prüfer, 2014).

The applied TLS system RIEGL VZ-2000 is a pulse-based scanner with a 360º horizontal

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field of view and a 100º vertical field of view. It has a raw positional accuracy of 8 mm at 150 m scanning distance and a precision of 5 mm, and it can reach a range of 2000 m under

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ideal conditions on a target of 90% reflectivity. The high speed data collection (up to 396,000 points per second) makes it an ideal system for fast engineering surveying and three-dimensional modeling. 3.2. Data acquisition and processing The first dataset, referred to as the reference point cloud, was acquired on 25th August 2014, 22 days after the Hongshiyan landslide. Suitable scanning points were selected to ensure complete coverage of the study area, and a total of eight scan stations were set along the toe of the Hongshiyan rock slope at the opposite bank of the Niulan River to acquire the

ACCEPTED MANUSCRIPT entire 3D geometric information of the landslide (Fig. 4a). The distance from the scan points to the rock slope ranges between 250 and 300 m at the toe of the slope and 800 and 900 m at the top edge of the slope. Both the horizontal and vertical angular resolutions are set to 0.01º and the mean point spacing of the acquired data ranged from 5 cm to 15 cm. Four circular targets with known center coordinates are placed into the scanned scene as tie points for the

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geo-referencing process in the data processing stage (Fig. 4a). Overlapping scans of at least

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30% of the area between the adjacent scan stations were chosen in order to minimize

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occluded areas in the rugged terrain and to align and merge the acquired cloud points into a single file and create a final 3D model. The other datasets were acquired annually after the

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landslide in June 2015, May 2016 and June 2017.

The acquired point clouds are in each of their own relative coordinate systems; alignment

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is required to combine and merge the different point clouds into one single file. This was done by means of an completed through an algorithm to obtain an optimal roto-translation

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alignment matrix in three stages: (a) a preliminary rough alignment was done by means of

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manually picking of homologous point pairs in overlapping areas between two adjacent scans (e.g. rock spires, corners of rocksheds and corner of structures) (Kromer et al., 2015); (b) the alignment was subsequently optimized using the Iterative Closest Points (ICP) (Besl and

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McKay, 1992) to reduce differences between points progressively by a minimization of a mean square cost function; and (c) the final improvement was done by means of reducing the “search distance” parameter up to a few centimeters progressively to obtain an optimal roto-translation alignment matrix with an acceptable alignment error of 0.002 m (Lague et al., 2013). After alignment, the point clouds are merged and unified into one single file, about 30% overlapping and misleading points are removed. The unified point cloud is then transformed into the geographic coordinate system by using the four sets of circular targets as ground control points. As a consequence, the point clouds are merged, unified and then

ACCEPTED MANUSCRIPT colored with RGB information obtained from the calibrated digital camera set up on the laser scanner (Fig. 4a). For further analysis, the resulting point clouds are extracted and then meshed to derive accurate 3D surface models of the Hongshiyan post failure rock slope. The resulting surface model captures high-resolution geometry and morphology information of the post failure

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rock slope. The rugged landslide surface with bulges, depressions and existing discontinuities

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such as fault and bedding planes is in great detail (Fig. 4b). It can be used to identify and

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locate slope morphology and changes. 3.3. Discontinuities identification and measurement

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Discontinuities such as faults, foliation and joints play a key role in the morphology and the predisposition to failure of detached blocks at the rock slope. The spatial information and

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discontinuities that can be identified sufficiently for the 3D point clouds or the surface models have recorded the geometry and morphology information of the post failure rock

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slope in great detail.

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A simple method to achieve this identification is to estimate a best- fitting plane of a manually delimited point cloud subset that belongs to a discontinuity surface according to the operator’s judgment. Then, the orientations of discontinuity sets (dips and dip directions) can

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be directly determined by the normal vectors of the fitting planes (Gigli and Casagli, 2011). Fig. 4c shows some joints identified by this method. Since this is a manual method, it may be subjective and laborious.

In order to automatically and integrally identify joint sets, many useful methods have been put forward, such as Vöge et al. (2014) and Riquelme et al. (2014). In this study, a fuzzy cluster method based on Hammah and Curran (1998) has been developed. The fuzzy clustering analysis seeks to classify the dataset into K subgroups or clusters on the basis of the measured similarities among the observations of the dataset (Bezdek and Pal, 1992).

ACCEPTED MANUSCRIPT With triangular mesh modeling of point cloud data, normal vectors and occurrence of each triangular mesh are calculated, and then the normal vectors are separated into subgroups or sets based on the similarity across variables (Hammah and Curran, 1999; Jaboyedoff et al., 2007). The geometric information of joints is obtained and the automatic fuzzy clustering statistical analysis is executed. This automatic identification method is achieved by

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conducting a fuzzy K-means algorithm using Matlab (Mathworks Inc., 2013) in this study. As

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shown in Fig. 5, this algorithm is implemented by the following process: (a) calculating the

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normal vectors of the triangular meshes of the 3D surface model of the rock slope; (b) determining cluster numbers K and initial cluster centroids V0 (a simple method is to select

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the random K vectors as the initial guesses of the centroids; another useful way of selecting initial cluster centroids relies on field investigation or the preceding manual/semi-automatic

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identification methods); (c) calculating the distances d and computing the membership degrees matrix U of all normal vectors from the K cluster centroids (Hammah and Curran,

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1998); (d) using Picard iterations to solve for the minimum of the objective function and to ^

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and membership degrees matrix

^

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(Bezdek, 1981);

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obtain an optimal cluster centroids

and (e) classifying the occurrence of rock discontinuities by the rules of most subjection and coloring the clustering classification results of discontinuities (Jaboyedoff et al., 2009).

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The results of the automatic discontinuities identification of the landslide surface of the Hongshiyan post failure rock slope are shown in Figs. 6a-6e. Two dominant discontinuity sets are evident in the landslide surface. J2 areas, marked in green, are widely distributed and approximately parallel to the landslide surface. J3 areas are marked in red and are approximately vertical to the river direction. Due to the small overhanging surfaces, the roughness of structural planes and meshing error, there are inevitable mottled areas occurring in a unique discontinuity area identified by the proposed method, such as yellow or red colors that may be found in the J2 area. This is because the proposed approach uses the normal

ACCEPTED MANUSCRIPT vectors of the triangular meshes to represent the orientations of the discontinuities, and thus the identification accuracy will be greatly decreased with regard to the slightly closed or unexposed discontinuities. For example, in the large thick layer structure of the Hongshiyan post failure rock slope, the closing joint set J1 is omitted in the automatic identification processes. Therefore, the automatic identification method combined with the manual

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identification method can give a more reasonable result.

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In order to measure the spacing and length of the identified joint sets, triangular meshes

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that correspond to each individual joint set is selected (e.g. the selected triangular meshes related to J3 showed in Fig. 6f). A reference plane according to the mean orientation of the

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investigated joint set is fitted and its normal vector is calculated. Along a cross section of the joint set (scanline profiles), a point is anchored in each joint, and a plane parallel to the

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previous reference plane is created based on the anchored point. Then the distance between the planes is calculated and the mean spacing of each set is obtained. In the same time, the

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trace length of each joint is acquired (Fig. 6g) based on the selected triangular meshes of each joint set using manual measurements (Matasci et al., 2018). For each joint set, hundreds of

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measurements along several scanline profiles are implemented in accords with standardized statistical protocols (ISRM 1978; Matasci et al., 2018).

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3.4. Rockfall detecting

Rock slope changes such as rockfall and deformations can be detected by the geometrical comparison of multi-temporal surface models using a shortest distance (SD) algorithm (Abellán et al., 2010). For each point i (x i.det , yi.det , zi.det ) on the detecting model, the SD algorithm searches its corresponding nearest point j (x j.ref, yj.ref, zj.ref), on the reference model, and computes the SD vector Vi between both points by Eq. 1 (Oppikofer et al., 2009). V i  (  x ,  y ,  z )  ( x i .d et , y i .d et , z i .d et )  ( x j .ref , y j .ref , z j .ref )

(1)

ACCEPTED MANUSCRIPT The resulting SD vector contains a Euclidean magnitude and orientation which refer to the change value and direction of detecting object. These SD vectors are not usually in the true kinematic direction of displacement, rather they are the shortest distance between models. The SD algorithm is useful for the analysis of slope changes since it allows detecting vertical, horizontal and oblique differences between models. In this study, the negative SDs indicate

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that the detecting objects are behind or below the reference dataset such as vertical settlement,

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erosion or subsidence and rockfall. The positive SDs signify that the detecting objects are

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situated above or in front of the reference dataset, which can result from gaining of material or slope sliding or deformation to the free surface (Oppikofer et al., 2009)

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At the same time, rockfall volumes are calculated using the surface-to-plane volume calculation algorithm (Abellán et al., 2011). This procedure requires a reference plane set

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behind the two surface models before and after rockfall. For each surface model, the volume between the surface model to the plane and bounded by a rockfall perimeter polyline is

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calculated by integrating at a specified sample step perpendicular to the plane (Kromer et al., 2015). Rockfall volume refers to the difference between the two previously calculated

study.

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volumes. A specified sample step of 0.01 m is used to calculate the rockfall volumes in this

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To visualize and detect the temporal evolution of the Hongshiyan post failure rock slope and to evaluate the rockfall volumes and the controlling mechanisms, different high resolution surface models were created for user-defined time periods to capture geometry and topographic information of the landslide surface and display the post failure rock slope changes over the monitored time interval. Rockfall visualizations between surface models of reference and user-defined time periods are detected using the SD algorithm, as well as the rockfall volumes, which are calculated from surface model subtractions by the surface-to-plane volume calculation algorithm.

ACCEPTED MANUSCRIPT 4. Results 4.1. Discontinuities analyses The automatic identification algorithm computes the normal vector of each triangular mesh with respect to its neighbors and attributes a unique RGB color to each dominant direction. It

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permits the user to visually identify the discontinuity sets and to select areas with the same color and the same orientations for the computation of the average dip direction and dip angle

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(Abellán et al., 2009). Through combining the automatic identification method and the

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manual identification method, a total of 331 discontinuities are identified. As shown in Fig. 7a, their dip and dip direction values are then plotted in a stereographic projection. There are

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three main preferred discontinuity sets that can be found in the landslide surface (as shown in

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Table 1): joint set J1 is developed along the rock strata with a mean dip direction of 307° and a mean dip angle of 20° with a small variability (1σ = 7.6°), the mean trace length of joint set J1 reaches 75.41 m and the mean spacing is about 4.51 m; joint set J2 is approximately

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parallel to the landslide surface with a mean dip direction of 186º and a mean dip angle of 78º,

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it is widely distributed with a relatively big variability of one standard deviation of 17.5°, the mean spacing of joint set J2 is 6.81 m and the mean trace length is about 27.53 m; joint set J3 has a mean dip direction of 60º and a mean dip angle of 77º with only small variability (1σ =

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8.2º), it has a mean spacing of 8.32 m and a mean trace length of 54.56 m. These identified discontinuity sets are very similar to those from traditional geological surveys (Fig. 7b). However, they could provide more detailed information about the distribution and structural features of the discontinuities and a more comprehensive view of the landslide surface. The distribution features of the identified joints present an obvious regional pattern (Fig.6). Numerous large-scale joints J2 are developed in the upper part of the landslide surface (Fig. 6b), and the mean trace length reaches 35.42 m. The steep open joints J2 is intersecting with

ACCEPTED MANUSCRIPT joints J3 . As shown in Fig. 6c, in the middle lower part of the landslide surface, the joint set J2 , and joint set J3 , are densely distributed and interlaced, the mean spacing of joint set J2 is as small as 2.32m. The quantities and scales of discontinuities in the right wing of the landslide surface are much smaller than that in other regions (Fig. 6d), the majority of both joint set J2 and joint set J3 have a trace length less than 10 m. The joint set J3 is well developed in the in

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the left wing of the landslide surface (Fig. 6e), the spacing is less 3 m, while the trace length

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is vary from 3 m to 50 m.

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4.2. Rockfall analyses

Fig. 8 shows a multi-temporal comparison of the surface models of the Hongshiyan post

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failure rock slope covering the time interval from August 2014 to June 2017. It provides a high resolution and accurate visualization of the nature and temporal evolution of the

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Hongshiyan rock slope. The surface variations are color-coded and the involved mass volumes are calculated. The blue color represents positive changes that relate to a gain of

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materials and the red color depicts areas of material loss that indicate rockfalls. As shown in

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Figs. 8a-8c, rockfalls frequently occurred in the landslide surface or along the edge of the post failure rock slope since the Hongshiyan landslide occurred. The main failure mechanisms of the rockfall investigated in the rock slope can be divided into plane failure,

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wedge failure and toppling failure. The plane and wedge failures mainly occurred in the landslide surface while the toppling failures are mainly observed along the edge of the post failure rock slope. As shown in Fig. 8d, the table lists volumes of 36 main rockfalls detected in the rock slope. It can be seen that the rockfall volumes vary in size, depending upon the locations. Rockfall is highly influenced by local topography, scales and the structural features of discontinuities and external disturbance (Goodman and Bray, 1976; Hudson and Priest, 1983). The failure mechanism and evolution of rockfalls in different local regions can vary under

ACCEPTED MANUSCRIPT certain conditions (Stead and Wolter, 2015). According to the volume and failure mechanism of detected rockfall events, as well as the distribution features of the identified discontinuities, the rockfall area in the Hongshiyan post failure rock slope can be broadly divided into the A, B, C and D zones. Zone A is located in the upper part of the landslide surface. Plane failures frequently occurred in this zone and the rockfall volumes are generally larger; the largest

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volume is 508.5 m3 (rockfall of ID 32). Rockfalls in this area have an obvious progressive

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and continuous failure phenomenon (e.g., rockfalls of ID 7, 8 and 16 occurred followed by

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progressive rockfalls of ID 31, 19 and 32, respectively). Zone B is located in the middle lower part of the landslide surface. A large number of wedge failures occur in this area while

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the rockfall volumes are usually small. Zone C is in the right wing of the landslide surface next to zone A. Multiple planes and toppling failures are detected in this area, while the

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volume and dispersion are much less than that in the zone A. Zone D is in the left wing of the landslide surface where a slope cutting measure was implemented. Rockfall detection is

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hampered in this area.

Bar charts of a total of 112 detected rockfall events for the different failure mechanisms

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distribution considered are shown in Fig. 9. It can be seen from Fig. 9a that the main failure mechanisms of the rockfalls are plane or toppling failures, and a total of only 6 toppling

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failures are detected along the edge of the post failure rock slope. The majority of rockfalls occurred in the first two years and the largest rockfall frequency took place during the time interval from June 2015 to June 2016 because of the slope cutting disturbance. A frequency chart of the volume of detected rockfalls is shown in Fig. 9b. It shows that the majority of rockfall volumes are quite small and that the most frequent rockfall volume lies on 1 m3 -0.1 m3 . The toppling failed rockfalls are relatively large (more than 10 m3 ) while the majority of wedge failed rockfalls are generally small (less than 1 m3 ). Plane failures are widely spread with volumes varying from more than 100 m3 to less than 1 m3 . Frequency charts of the

ACCEPTED MANUSCRIPT detected rockfalls distribution features in zone A, B, C and D are shown in Figs. 9c and 9d. It can be seen that most of the rockfall events (total of 86 events) are occurred in zone A and B. Plane failure and wedge failure are mainly occurred in zone A and B, respectively, while the toppling failure mostly happened in zone C. Most of the large volume rockfall events are

events are detected in zone D because of excavation disturbance.

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4.3. Kinematic analysis

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distributed in zone A, which is in accordance with Fig. 8. Only three wedge failure of rockfall

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The orientations of the discontinuities in the landslide surface are strongly re lated to the tectonic evolution processes and the 2014 Ludian Ms 6.5 earthquake. A kinematic analysis is

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helpful to study the failure mechanisms of the rockfalls and to propose a proper treatment measures in the future.

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The kinematic analysis is taken on the premise that the rock is rigid, i.e. the deformation of rock bocks is neglected. It is carried out by demonstrating the slope orientations, the

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discontinuity orientations and discontinuity friction angles in a structural plane stereographic

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projection, and using their composition relationships to analyze the probabilities of occurrence of toppling failure (Goodman and Bray, 1976; Goodman, 1980), plane failure (Hoek and Bray, 1981) and wedge failure (Hoek and Bray, 1981).

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Thanks to the detailed TLS survey, the discontinuity features (occurrences, spacings, trace lengths and their geometric compounding relationships) and rockfall events can be quantitatively analyzed and detected in each local regions with the advantageous use of the method of automatic discontinuity identification and the SD algorithm (Fig. 6 and Fig. 8). For better analysis of the failure mechanisms of rockfalls in certain conditions, a kinematic analysis is implemented in previous four local regions (zone A, B, C and D). By fitting a plane using the corresponding local region point cloud data (same as the manually discontinuity identification in section 3.3) (Gigli et al, 2014), the mean local slope

ACCEPTED MANUSCRIPT orientations of 81º/183º (dip/dip direction) for zone A, 77º/172º for zone B, 76º/195º for zone C and 78º/142º for zone D are obtained, and a mean friction angle of 26º determined and suggested by the designers is chosen at the same time. Fig 10 shows the kinematic analysis of toppling failure (Figs. 10a-10d)), plane failure (Fig. 10e-10h) and wedge failure (Fig. 10i-10l) for zone A, B, C, and D, respectively. As shown in Fig. 10a-10d, there are few joint poles

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falling within the toppling failure region. Therefore, the risk of toppling failure is minimal.

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However, several toppling failures occurred along the edge of the landslide surface because

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of multiple immeasurable tension fractures by TLS that developed approximately parallel to the edge of the landslide surface. Plane failure zone is represented by the crescent-shaped

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region in Figs. 10e-10h. A large number of joint set J2 poles fell in the regions of zone A (Fig. 10e) and B (Fig. 10f), indicating that plane failure is one of the main types of rockfalls for

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zone A and B (Fig. 8 and Fig. 9c). The crescent-shaped zone outside the slope plane but enclosed by the friction cone represents a wedge-failure region (Figs. 10i-10l). The joint set

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J1 , joint set J2 , and joint set J3 form the unstable wedge blocks. Wedge failure would be another common rockfall type in the landslide surface, particularly for zone B (Fig. 10j) and

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D (Fig. 10l), for any plane intersections that fall within the wedge region will make the surface unstable (Fig. 8 and Fig. 9c). Thus, wedge failure should not be ignored in zone D

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despite few rockfall events being detected because of slope-cutting interference.

5. Discussions

Traditional geological surveys are restricted in the research of high steep rock scarps, especially for heavily fractured and active rockfall areas. The TLS technique, on the other hand, allows the remote, quick, and accurate measurement of all of the main geometric characteristics of the rock slopes (Gigli et al., 2014).

ACCEPTED MANUSCRIPT The topography of local landslide surface, scale and structural features of the discontinuities have a great effect on the rockfall distributions and failure mechanisms. Thanks to the detailed TLS survey, the accurate 3D spatial information of rock slop can be obtained without contact, and then with triangular mesh modeling of point cloud data, the geometric information, normal vectors and occurrence of each triangular mesh can be

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calculated. By conducting a fuzzy K-means algorithm using Matlab in this study, the normal

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vectors are separated into subgroups or sets based on the similarity across variables. The

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automatic identification of discontinuity sets is achieved, and dominant occurrences of rock mass discontinuities in each local region can be acquired in great detail. This automatic

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identification method permits the user to visually identify the discontinuity sets and acquire their spacial distribution features (occurrences, spacings, trace lengths and their geometric

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compounding relationship) for further geotechnical analysis. The result presented here indicated that the discontinuity automatic identification method will help rock mechanics

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engineers by supplementing part of the classical field work and facilitating more quantitative analysis of the structure features (Jaboyedoff et al., 2007). In the mean time, based on the SD

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algorithm and the surface-to-plane volume calculation algorithm, the distributions, shapes, volumes and scars of the rockfalls can be accurately detected over the monitored time interval

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(Fig. 8 and Fig. 9). Integrated with the previous discontinuity automatic identification method, the evolution and failure mechanisms of rockfalls can be quantitatively analyzed and assessed.

As shown in Fig. 2a and Fig. 6a, the rock mass at the top of the rock slope is highly damaged because of the seismic amplification effect. The landslide surface in the rockfall area of zone A is very rough (Fig. 6b), numerous large-scale joints J2 and joints J3 , are developed in this area. The steep open joints J2 , combined with joints J3 , cut the rock mass into multiple large plate-like potential unstable blocks. As a result, plane failure of rockfalls

ACCEPTED MANUSCRIPT occurs under unfavorable conditions, such as excavation disturbance, aftershocks and rainstorms (Fig. 3b). As shown in Fig. 6c, a large number of joints J2 and joints J3 are densely distributed in the rockfall area of zone B. They are interlaced and combined with the joints J1 in cutting the rock mass into many potential unstable wedge blocks (Fig. 10j). Therefore, the wedge failures of rockfall frequently occur in this area (Fig. 8 and Fig. 9c). The quantities

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and scales of discontinuities in the rockfall area of zone C are much smaller than those in the

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rockfall area of zone A (Fig. 6d), which indicates that the frequency and volumes of the

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rockfall in this area are much less than those in the rockfall area of zone A (Fig. 8 and Fig. 9d). It is remarkable that massive joints J3 are developed in the rockfall area of zone D with

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relatively large scales and quantities (as shown in Fig. 6e). Wedge failure is likely to occur in this area (Fig. 10l), thus, more attention should be paid to zone A despite few rockfall being

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detected because of slope-cutting interference.

The feature of the joints distribution plays a principal role on the rockfall events but was

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definitely not a sole factor. Rockfall events are also affected by construction disturbance and surrounding environment changes. As shown in Fig. 11a, after the earthquake and landslide,

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the stability problem of the rock mass in the landslide surface is prominent and the rockfalls are likely to occur under unfavorable conditions such as aftershocks and rainstorms, because

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of the steep and rough landslide surface, and well developed joints J2 ; original disadvantageous structures such as joints J1 and joints J3 ; and block bodies combined under these factors. The previous rockfall may lead to an overhanging area and induce a new unstable rock block because of the anti-dip stratified structure that is characteristic of the rock slope (Fig. 3c). As shown in Fig. 11b and Fig. 11c, during the unloading recovering and slope treatment stage, more joints would be induced by the blasting vibration and other unfavorable effects, and new rockfalls are more likely to occur in the overhanging area. As a result,

ACCEPTED MANUSCRIPT numerous rockfalls, mainly in the rockfall area of zone A near the slope-cutting outline, has presented obvious progressive and continuous failure phenomenon (Fig. 8b and 8c). To guarantee the safety of the construction workers and mechanical equipment under the base of the rock slope during the reinforcement and later operation of the project, as well as reduce secondary earthquake or rainstorm disasters, comprehensive treatment measures must

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be taken to relieve the rockfall threat and ensure the long-term stability of the post failure

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rock slope. Loose bodies and local potentially unstable rock masses in the landslide surface

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or along the edge of the post failure slope should be eliminated. Closing treatment and concrete replacement are employed for the large and opening J2 , in the rockfall area of zone

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A, to prevent the new occurrence of progressive and continuous failure phenomenon. Random rock bolts, as well as jetting concrete nets, must be used to reinforce potentially

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unstable rock blocks in the rockfall areas of zones B and C. A passive prevention net must be set along the top edge of the lower part of the original cliff to prevent gravel slide. Finally,

in the rockfall area of zone D.

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much greater attention should be paid to the rockfall monitoring and early warning, especially

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The research results show that the TLS technique is more labor-saving and time-saving to investigate rock discontinuity and detecting the rockfall events on large and steep slope than

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the conventional method (Gigli and Casagli, 2011). It is also proved to be of great benefit for the field investigation, failure mechanism analysis and corresponding treatment measures formulating of high steep rock scarps, especially for heavily fractured and active rockfall areas.

6. Conclusions The geometric information and discontinuity characterization of rock masses are the important factors for the analysis of the evolutions and failure mechanisms of rockfalls.

ACCEPTED MANUSCRIPT Combined with field investigations and TLS technology, the accurate 3D surface models of the Hongshiyan post failure rock slope have been established, the discontinuity sets have been identified and determined accurately, and, finally, the rockfall failure mechanisms and evolutions have been detected and analyzed q uantitatively. The following can be concluded from this study: By conducting a fuzzy K- means algorithm, the automatic identification of discontinuity

PT

1.

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sets is achieved, and dominant occurrences of rock mass discontinuities in each local

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region can be acquired in great detail. This automatic identification method permits the user to visually identify the discontinuity sets and acquire their spacial distribution

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features, e.g. occurrences, spacings, trace lengths and their geometric compounding relationships.

With the advantageous use of the SD algorithm and the surface-to-plane volume

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2.

calculation algorithm, the distributions, shapes, volumes and scars of the rockfalls can be

3.

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accurately detected over the monitored time interval. The main failure mechanisms of the rockfall investigated in the Hongshiyan post failure

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rock slope can be divided into plane failure, wedge failure and toppling failure. The plane and wedge failures mainly occurred in the landslide surface while the toppling

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failures are mainly observed along the edge of the post failure rock slope, the rockfall volumes, main failure mechanisms and evolutions varies in different local regions, obviously responded by preferred discontinuities. The anti-dip stratified structure characteristic and the excavation disturbances during the recovering and slope-treatment stage induce numerous progressive and continuous failures of rockfalls. 4.

The TLS technique is of great benefit for the field investigation, failure mechanism analysis and corresponding treatment measures formulating of high steep rock scarps, especially for heavily fractured and active rockfall areas.

ACCEPTED MANUSCRIPT Acknowledgments We gratefully acknowledge the support of the National Key R&D Program of China (2017YFC1501102), the National Natural Science Foundation of China (41472272), the Youth Science and Technology Fund of Sichuan Province (2016JQ0011) and the Graduate

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Student’s Research Innovation Foundation of Sichuan University (2018YJSY076). Critical comments by the anonymous reviewers greatly improved the initial manuscript.

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ACCEPTED MANUSCRIPT Figures

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Fig. 1. Location and topography of the Hongshiyan landslide: (a) location of the Hongshiyan landslide; (b) topography of the Hongshiyan landslide and (c) main dimension of Hongshiyan landslide.

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Fig. 2. Geological conditions of the Hongshiyan post failure rock slope: (a) view of the rock slope; (b) geological map of the rock slope; (c) geologic section of the rock slope and (d)

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stereographic projection of the mainly developed three group joint sets.

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Fig. 3. Rockfalls in the post failure slope: (a) wedge failure; (b) plane failure; (c) progressive

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Circular target

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Fig. 4. TLS scanning for the post failure rock slope: (a) distribution of scan points and circular targets in the holistic true colored point cloud of the rock slope; (b) 3D surface model

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START

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Triangular mesh modeling of point cloud

field investigation or manual identification methods

Calculating the distances d and the membership degrees matrix U

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Cluster numbers K and initial cluster centroids V0 determining

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Fig. 5. The flowchart of the fuzzy k-means algorithm.

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Fig. 6. Result of automatic discontinuities identification method: (a) a view of total identified discontinuities; (b) in the upper part of the landslide surface; (c) in the middle lower part of the landslide surface; (d) in the right wing of the landslide surface; (e) in the left wing of the landslide surface; (f) joint set spacing measurement and (g) joint set trace length measurement.

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Fig. 7. Stereographic projection of concentration lines of discontinuity poles and modal planes of discontinuity sets: (a) extracted from TLS data by using automatic identification

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method combine with the manual identification method; and (b) from traditional geological

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Fig. 8. Cumulative detected rockfalls events by TLS: (a) in June 2015; (b) in May 2016; (c)

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Fig. 9. Bar charts of detected total 112 rockfalls for the different failure mechanisms and

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NU

J1 Plane failure region

J3

J2

J2

MA

Slope face

J2

T oppling failure region Slope face J3

J1

J3

J3

J3

(d)

J2 T oppling Slope face failure region

J2 T oppling failure Slope face region

PT

J2 T oppling failure Slope face region

RI

(a)

Slope

Slope face

J1

J3 face J2

Wedge failure region

J3

J2 Wedge failure region

Fig. 10. Kinematic analyses of toppling failure, plane failure and wedge failure for zone A, B, C, and D: (a) to (d) are toppling failure for zone A, B, C and D, respectively; (e) to (h) are

AC C

plane failure for zone A, B, C and D, respectively and (i) to (l) are wedge failure for zone A, B, C and D, respectively.

ACCEPTED MANUSCRIPT

(a)

Rainfall

Seismic wave J1

RI

PT

J2

(b)

Over hanging

(c)

EP T

Slope cutting

ED

MA

J2

NU

J1

SC

Blasting vibration

Over hanging J1

AC C

J2

Fig. 11. Progressive and continuous failure mechanism analyses: (a) occurrence of previous rockfalls; (b) new induced unstable rock blocks and rockfalls; and (c) progressive and continuous failure of rockfalls.

ACCEPTED MANUSCRIPT

Tables Table 1. Orientation and variability of identified joint sets in the Hongshiyan post-earthquake

Dip direction

Dip

Variability 1σ

Average

Average trace

(º)

(º)

(º)

PT

Joint set

spacing (m)

length (m)

4.51

75.41

RI

rock slope.

6.81

17.53

8.32

54.56

Number

78

307

20

20.1

J2

143

186

78

7.6

J3

110

60

77

8.2

AC C

EP T

ED

MA

NU

SC

J1

ACCEPTED MANUSCRIPT Highlights 

The accurate 3D surface models of the Hongshiyan remnant rock slope have been established using the TLS technology.



Discontinuity sets are visually identified using the automatic discontinuity identification method. Distributions, shapes, volumes of rockfalls are accurately detected over the monitored

PT



SC

Failure and evolution mechanisms of rockfalls have been quantitatively analyzed and

EP T

ED

MA

NU

assessed.

AC C



RI

time interval.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11