A correlation between slope failures and accelerometric parameters: the 26 September 1997 earthquake (Umbria–Marche, Italy)

Soil Dynamics and Earthquake Engineering 20 (2000) 301±313

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A correlation between slope failures and accelerometric parameters: the 26 September 1997 earthquake (Umbria±Marche, Italy) L. Luzi, F. Pergalani* Istituto di Ricerca sul Rischio Sismico, 20133 Milan, Italy

Abstract After the occurrence of the 26 September 1997 earthquake in Umbria±Marche an extensive survey was performed to individuate surface effects induced by the ground motion. Several types of effects occurred on bedrock, calcareous debris and sandy-clay deposits. Shallow soil slides and dry debris slides were mapped by ®eld survey and aerial photograph interpretation in the epicentral area close to Nocera Umbra. This gave the opportunity for testing the prediction maps that can be obtained with the use of empirical laws, proposed by several authors, based on a simple method (Geotechnique 15(2) (1965) 139). Accelerometric records from the permanent and mobile seismic network of the Seismic Survey of Italy have been processed and interpolated to obtain strong ground motion parameters at each site, e.g. Peak Ground Acceleration, Spectral Intensity, Arias Intensity, etc. The different predictions have been tested with the real landslide data to verify their performances. Some of the proposed methods explain quite well the behaviour of slopes during the application of a time history and can therefore be employed for future landslide hazard zonation. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Landslide; Earthquake; Prediction; Local effects

1. Introduction Earthquake triggered landslides represent one of the most important secondary effect of large earthquakes. They can cause damage to buildings and infrastructure with consequent loss of lives. Slope failures induced by ground motion have been extensively mapped during recent seismic events [17,26,30,35,36]. Landslide evidences are not only useful to study the mechanisms of slope failures, but also to relate displacements to triggering factors in order to assess rules for future slope failure predictions. Several models of dynamic slope stability calculation have been proposed by many authors in the past [24,31], mainly for the stability evaluation of single slopes. They assume a simpli®ed geometry for landslide bodies, i.e. a single block, and calculate landslide displacements given a time history, by integrating the equation of motion. Rules for the calculation of post-seismic displacements have been lately proposed by Ambraseys and Srbulov [2]. The displacement being calculated with a time history, Newmark's [24] model has been lately generalised, by ®nding empirical relationships relating the displacements, obtained by several runs of the model, with ground motion parameters. * Corresponding author. E-mail address: ¯[email protected] (F. Pergalani).

Any accelerometric record can in fact be generalised with a unique value, indicating either the energy content of the strong motion, or its duration, or a crisp peak parameter (i.e. Peak Ground Acceleration, Peak Ground Velocity, etc.). Any slope, as well as the accelerometric record, can also be synthesised with a unique parameter expressing its susceptibility to failure during an earthquake taking into account its geometry, geotechnical properties and hydrogeologic conditions. It is usually expressed by the critical horizontal acceleration coef®cient that can bring the slope to the limit equilibrium, that is when the shear resistance equals to the acting shear stress on the failure surface. The empirical relationships relate critical horizontal acceleration coef®cient, coseismic displacement and accelerometric parameters and avoid the integration procedure. Several attempts of landslide hazard zoning have been performed during the last decade, with the aid of simpli®ed deterministic models, once used for a single slope calculation [15,20,22,23,34]). This is mainly due to the large spread of Geographical Information Systems; that are de®ned as powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes [4]. This new technology has its advantages and disadvantages: on one hand, it allows the manipulation of large amounts of data as well as the zonation of large territories, but, on the other hand, the generalisation of terrain properties over large

0267-7261/01/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S 0267-726 1(00)00063-4

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Table 1 Main earthquakes …M $ 5† of the Umbria±Marche seismic sequence Date

GMT

ML

Ms

Mw

Long. E

Lat. N

97.09.26 97.09.26 97.10.03 97.10.06 97.10.12 97.10.14 98.03.26 98.04.03

00.33 09.40 08.55 23.24 11.08 15.23 16.26 07.26

5.5 5.8 5.1 5.3 5.1 5.5 5.5 5.0

5.5 5.9

5.7 6.0

12.89 12.84 12.84 12.84 12.97 12.94 12.85 12.79

43.02 43.01 43.05 43.02 42.87 42.91 43.20 43.20

5.2 5.5

5.6 5.3 5.1

areas can induce to stability evaluation errors. A solution proposed for solving that problem is the introduction of error propagation into stability calculations. It is based on the concept that geotechnical properties, as well as geometric and hydrogeologic ones, are random variables for each terrain unit and therefore they can be expressed by probability density distributions. The resulting stability parameters are also expressed as a probability density function [7,23,34]. To perform an error propagation analysis large amounts of geotechnical and hydrogeologic data should be collected, and it is usually the case when dealing with large projects. Unfortunately the Umbria±Marche event occurred in an area where no projects for geotechnical parameter collection were previously conducted, therefore only the average terrain properties were available. On the other hand, slope displacement data and real accelerometric records were collected after few weeks and this was an opportunity for testing several empirical rules, in order to obtain useful indications and threshold parameters to perform a prediction for future slope failures in the area and to extrapolate the results to similar areas. 2. The Umbria±Marche seismic sequence and surface effects The Umbria±Marche seismic sequence started on 4 September 1997 with a ML 4.4 earthquake located near the village of Col®orito, close to the boundary between Marche and Umbria Regions. Lately, on 26 September at 00.33 GMT, a Ms 5.5 earthquake occurred with epicenter located between the villages of Cesi and Col®orito. It was followed by a stronger earthquake at 9.40 GMT (Ms 5.9; Mw 6.0), which represents the main shock of the entire seismic sequence and caused damages as large as IX in the MCS macroseismic intensity scale. The epicenter was located north of the previous one, between the villages of Col®orito and Annifo. In the following weeks the seismic activity was very intense with more than 2000 shocks since 26 September to 14 October with about 20 earthquakes exceeding magnitude 4.0. In Table 1 a list of earthquakes with magnitude equal to or greater than 5.0 is reported.

Depth (km) 7.0 8.0

5.0 50.0 6.0

I0 (MCS) VIII VIII±IX VII VII±VIII VI±VII VIII VII VII

The sequence initially showed a northward migration pattern of the epicenters. Until 12 October, the seismic activity, which was initially concentrated in the northern part of the area, migrated to the southern part, between the villages of Sellano and Preci, where, on 14 October, at 15.23 GMT an earthquake of Ms 5.5 occurred [1,12]. Finally, on March and April 1998, two earthquakes larger than 5.0 in magnitude occurred more than 20 km northward of the ®rst sequence (Table 1). This irregular activity seems to emphasise the activation of several interconnected fault segments rather than a single segment of a main seismogenic structure. The epicentral distribution of the ®rst two sequences shows a NW±SE trend for a total length of about 30 km. Fault plane solutions, computed by CMT method, indicate a main dip-slip mechanism along a plane oriented about NW± SE with a slight right strike-slip component, with a T-axis oriented NE±SW. The depth of the foci shows a concentration between 4 and 8 km. These directions are in good agreement with the structural framework of the area, represented by a conjugate system of normal faults oriented along the axis of the Apennine. The repeated earthquakes gave cumulative effects: the ®nal estimated maximum intensity was IX±X in the MCS scale and, despite the huge amount of damage, fortunately only 11 people died, 126 injured, but the homeless were more than 25,000. The earthquake sequence produced several ground effects and soon after the main shock a survey was performed by various scienti®c and governmental organisations. A database of the collected data was built and a very preliminary analysis [26] classi®es the effects as: landslides, gravitational cracks, surface cracks due to shaking and fault propagation to the surface. Their relative distribution, as proposed by Prestininzi et al. [26] is: landslides (45%), gravitational cracks (11%), surface cracks due to shaking (41%) and fault propagation to the surface (3%). They occurred on different types of bedrock and deposits and on man made ®ll. Most of them were mapped mainly because they either affected infrastructures, or where located in their vicinity, or in correspondence of well known tectonic structures. The 200 collected samples were therefore supposed to be only a subset of the possible effects caused by the earthquake sequence.

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303

Fig. 1. Geologic set of the Nocera Umbra area (l ˆ lacustrine deposits; Ma ˆ Marnoso Arenacea; a ˆ alluvial deposits; Ma1 ˆ shales; Sc ˆ Scaglia Cinerea; Sr ˆ Scaglia Rossa; MF ˆ Marne a Fucoidi; ¯1 ˆ lacustrine deposits; CD ˆ Calcari Diasprini; RA ˆ Rosso Ammonitico; Co ˆ Corniola; CM ˆ Calcare Massiccio; tr1 ˆ travertine; ¯2 ˆ lacustrine deposits; dta ˆ slope debris; MAI ˆ Maiolica). The study area is drawn in purple. Geographical units are UTM, zone 33.

3. Geologic overview and sample area selection The investigated area is located in the Umbria±Marche Apennines, Central Italy. The stratigraphy is represented by the Umbria±Marche sedimentary sequence, composed of limestones, marly limestones, marls and ¯ysch sequences [6]. The structural pattern of the area started to delineate from the Oligocene; it is made up of several tectonic units put straight as a result of convergence and collision between the continental margins of the Corsica±Sardinia block and the Adriatic block [5]. The main compressive phase started in the Tortonian and the lack of Pliocene±Pleistocene marine deposits proves that after the Miocene the area was de®nitively uplifted. The compressive structures were dissected by normal faults during the Quaternary, and, according to the most recent studies [21], these are related to the crustal thinning processes occurring in the Tyrrhenian Tuscan area. The Quaternary normal faults led to the formation of intramountain basins, of which the Col®orito plain is a clear example, and the seismicity of the area is mainly related to the activity of these faults. The geomorphologic setting is characterised by a general conformity between structural-lithologic elements and morphologies. High relief zones are found in correspondence with the calcareous ridges and hilly and smooth areas correspond to the ¯ysch deposits, in the zone of Nocera Umbra and Camerino. Even the drainage network is in¯uenced by the structural pattern and the main drainage lines are located along the trace of the main faults and fractures. Climatic factors, especially the last glacial and interglacial period, in¯uenced the landscape evolution and the deposition type. Strati®ed periglacial slope waste deposits, mainly formed by Scaglia Rossa and Maiolica cobbles,

occur extensively on calcareous slopes [10,13,8]. The alluvial terraces also refer to the glacial periods; they are placed at different levels over the valley bottom and are often interbedded with slope deposits; three main levels are found in the area but the number can vary according to local conditions. Lacustrine deposits are found in correspondence of intramountain basins; they can reach thickness of about 100±200 m and are formed by more or less regular alternances of conglomerates, clays and sands; they are dated lower±middle Pleistocene. Finally, travertine deposits are widespread all over the area; they are mainly formed by spring water whose chemical content is connected to the activity of deep faults and fractures; the age of these deposits is referred to middle Pleistocene up to nowadays. The sample area is located close to Nocera Umbra (Fig. 1). It includes part of the calcareous sequence and the ¯ysch deposits of the Marnoso Arenacea formation. Several quaternary deposits crop out, like lacustrine deposits and alluvial terraces. The reason for the selection of this zone is mainly due to the vicinity to the epicentral area and the typology of slope failure, which is mainly represented by soil and debris slides. 4. Empirical methods for evaluating coseismic landslide displacement Several empirical methods have been proposed for evaluating coseismic landslide displacements. All of them are based on the theory of Newmark [24]. It assumes the landslide is a rigid block with its own frictional properties at the surface-block boundary, sliding on an inclined surface and that a cyclic load is applied at the base. The surface-block

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boundary has a rigid plastic behaviour, when the acting shear stress exceeds the resistance stress the system is in a limit equilibrium state and displacement can take place. The block displacement ends when the relative velocity with the base equals zero. Displacement steps are summed up over the duration of the acceleration time history. As stated before several block geometry and properties as well as several acceleration-time histories, can be input into the simulation and a regression analysis can then be performed in order to relate displacement values against variables indicating the landslide susceptibility to failure and seismic parameters. The regression equation has generally the following form:

where Dn is the displacement (cm), Ms the surface wave magnitude, r the source distance (km), given by (d 2 1 h 2) 0.5 where d is the epicentral distance (km) and h the source depth (km), q is the critical acceleration ratio, the ratio between critical horizontal acceleration coef®cient and Peak Ground Acceleration, PGA, (g); p is the error quanti®cation. This empirical rule has been derived by running the Newmark's [24] simulation with USA and Euro-Asian accelerograms. Jibson et al. [20] proposed the following relation, based on the Arias Intensity [3]:

f …D† ˆ Ag…s† 1 Bh…k† 1 C

where Dn is the displacement (cm), Ia the Arias Intensity (m/ s) and Kc is the critical horizontal acceleration coef®cient. The standard deviation is equal to 0.375 and 0.83 is the regression coef®cient. This equation is based on the results of the application of USA and Iranian accelerograms. There is no control on relative Kc ±PGA values and the threshold for slope susceptibility to failure is demanded of the analyst, who selects the minimum considerable slope angle. Very recently Crespellani et al. [11] proposed the following relation, based on the Destructiveness Potential [28]:

…1†

where D is the displacement, s the seismic parameter, k the landslide susceptibility parameter and A, B and C are constants. One of the assumptions made, when considering the coseismic displacement, is that the residual undrained/drained shear resistance does not change during the deformation, that is the landslide susceptibility to failure keeps constant, and no pore pressure increase is taken into account. Ambraseys and Srbulov [2] proposed the following relation: log…Dn † ˆ 22:41 1 0:47Ms 2 0:010r 1log‰…1 2 q†2:64 …q†21:02 Š 1 0:58p

(2)

log…Dn † ˆ 1:521 log Ia 2 1:9931 log Kc 2 1:546

Kc21:202 Dn ˆ 0:021P0:910 d

…3†

…4†

where Dn is the displacement (cm); Pd the Destructiveness Potential (10 23 g s 3) and Kc is the critical horizontal acceleration coef®cient. No control for the relative values

Fig. 2. Landslide occurrences after the 26 September 1997 event over the digital terrain model.

L. Luzi, F. Pergalani / Soil Dynamics and Earthquake Engineering 20 (2000) 301±313 Table 2 Geotechnical and geometric parameters of the engineering geological units (bed ˆ bedrock; dt/Ma ˆ colluvial deposit of the pelitic arenaceous formation; cd ˆ alluvial fan;dt/l ˆ colluvial deposits of the lacustrine unit l; dtv ˆ slope debris; a ˆ alluvial deposits; dt/Sc ˆ colluvial deposits of the Scaglia Cinerea formation; tr ˆ travertines; dt/¯2 ˆ colluvial deposits of the lacustrine unit ¯2; dt/Ma1 ˆ colluvial deposits of shales; dt/Ma2 ˆ colluvial deposits of arenaceous pelitic formation) Code

c 0 avg (kPa)

f 0 avg (8)

g (kN/m 3)

Depth (m)

bed dt/Ma cd dt/l dtv a dt/Sc tr dt/¯2 dt/Ma1 dt/Ma2

0.0 0.0 0.0 1.5 9.5 0.0 13.8 0.0 1.5 0.0 1.3

0.0 20.0 35.0 20.0 29.0 35.0 25.0 0.0 20.0 16.0 25.0

0.0 20.0 20.0 20.0 18.5 20.0 20.3 0.0 20.0 20.0 20.0

0.0 3.0 5.0 3.0 8.0 5.0 3.0 0.0 3.0 3.0 3.0

Dn ˆ …0:292 1 0:0762Ia † Dn ˆ …26:794 1 0:46Ia †

log…Dn † ˆ 0:607…^0:020† log Ia 2 3:719…^0:049† log K 1 0:852…^0:030†

and K is the ratio between the shear component of Kc and the Peak Ground Acceleration. The standard deviation is 0.365. The equation is derived from the Italian accelerometric records since 1972. Finally, the authors propose two empirical rules, based on Italian data. One relates the displacement, evaluated by the application of the Newmark's model to several landslide samples, to the Arias Intensity [3] and the other to the Spectral Intensity [18]. In fact there is no unique equation; the landslide susceptibility values are sliced into six classes and a check is performed on Kc ±PGA relative values. If PGA exceeds Kc displacement can take place and a set of equations is proposed to evaluate the displacement: Dn ˆ …0:424 1 0:0818Ia †2

Kc ±PGA is requested. The standard deviation of (ln Dn) is 0.746, while the regression coef®cient is 0.889. Correction factors should be applied to the ®nal displacement outcome when considering planar or rotational landslides. The equation is based on European, USA and Mexican accelerograms. Another rule based on the Arias Intensity [3] parameter was proposed by Romeo [27]:

(5)

where Dn is the displacement (cm), Ia the Arias Intensity (m/s)

305

if Kc # 0:01 …RMSE ˆ 0:632†

2

if 0:01 , Kc # 0:03 …RMSE ˆ 1:34†

2

if 0:03 , Kc # 0:06 …RMSE ˆ 1:09†

Dn ˆ …21:09 1 0:07Ia †

if 0:06 , Kc # 0:1 …RMSE ˆ 1:8†

Dn ˆ …20:07 1 0:0049Ia †

if 0:1 , Kc # 0:2 …RMSE ˆ 1:8†

Dn ˆ …20:0001 1 0:000012Ia †

if 0:2 , Kc # 0:03 …RMSE ˆ 0:0014†

Dn ˆ 0

if Kc . 0:3

Dn ˆ …0:477 1 0:0750SI†2 Dn ˆ …0:362 1 0:0690SI†

(6)

if Kc # 0:01 …RMSE ˆ 0:632†

2

if 0:01 , Kc # 0:03 …RMSE ˆ 1:05† 2

Dn ˆ …20:001 1 0:0505SI†

if 0:03 , Kc # 0:06 …RMSE ˆ 0:68†

Dn ˆ …20:048 1 0:0190SI†2

if 0:06 , Kc # 0:1 …RMSE ˆ 0:41†

Dn ˆ …20:041 1 0:0030SI†

if 0:1 , Kc # 0:2 …RMSE ˆ 0:19†

Dn ˆ …0:00027 1 0:000074SI†

if 0:2 , Kc # 0:03 …RMSE ˆ 0:013†

Dn ˆ 0

if Kc . 0:3

Fig. 3. Landslide susceptibility map, expressed as the critical horizontal acceleration coef®cient, Kc (g). Geographical units are UTM, zone 33.

(7)

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where Dn is the ®nal displacement (cm), Ia the Arias Intensity [3] (cm/s), and SI is the Spectral Intensity [18] (cm). 5. Application The test of the prediction performance for different rules has been conducted by selecting only shallow earth slide and debris slides that ®t the comparison with the rigid block assumption adopted by Newmark [24]. An extensive aerial photo interpretation was done on a photograph set at 1:10,000 scale taken either the day after or few weeks after the two main events, to individuate those landslides that were not mapped during the immediate survey. Many other earth slides or slope cracks, due to landslide initiation, were found far from the main infrastructures and villages and added to the data base. The complete inventory of the landslides is shown in Fig. 2. The test was performed on the entire area after a partitioning into homogeneous square cells of 10 m resolution.

The procedure adopted for the test was: (a) create a landslide susceptibility map, expressed by the Kc parameter; (b) calculate the earthquake parameters; (c) calculate the displacement map applying the empirical rules (Eqs. (2)± (7)); and (d) test of prediction performance by overlaying the displacement and the landslide map. 5.1. Creation of a landslide susceptibility map The Kc map was calculated using the well-known in®nite slope model [16] as follows: Kc ˆ

c 0 =cos2 b 1 …g 2 mgw †z tan f 0 2 gz tan b gz 1 gz tan b tan f 0

…8†

where c 0 is the effective cohesion (kPa), b the slope angle (8), g the unit weight of the soil (kN/m 3), m the water table coef®cient, given by the ratio of the water table depth and sliding surface depth, f 0 the effective angle of internal friction (8), and z is the sliding surface depth (m). The following base maps and attribute information

Fig. 4. Location of the accelerometric stations. The study area is drawn in grey. Geographical units are UTM, zone 33.

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should be entered in the calculation: 1. engineering geologic map, obtained by integrating the geologic map with aerial photo interpretation data; 2. slope angle map, obtained from the digital terrain model with 10 m resolution; 3. geotechnical and geometric attributes for each unit of the engineering geological map; 4. hydrogeologic map, expressed as the ratio between depth of the water table and depth of the terrain. Some consideration on the assumptions made should be done. The engineering geologic map was not checked in the ®eld, but the photo interpretation results were integrated and compared with the investigations available for the microzonation study [25] for many test sites. The slope angle was obtained by standard procedures available in the GIS package [14] and the resolution of 10 m was in agreement with that of the input data, as the contour interval, from which the digital elevation model was derived, is 10 m. The geotechnical properties and geometry of the engineering geological units are averaged values, obtained from the data available for the microzonation studies (Table 2). The authors are aware that averaged values represent a limitation for the analysis, as terrain properties may vary even in a limited amount of space and therefore their variability should be taken into consideration. Unfortunately the amount of data was too poor to perform any statistical analysis. Finally, the hydrogeologic map was obtained assuming that concave zones can keep moisture longer than convex zones and this was evident from the aerial photo interpretation, where moist zones were noticeable even if the earthquake occurred at the end of the dry season. The m coef®cient of Eq. (8) was set to 0.3 for concave zones and to 0 for convex ones. The susceptibility map, expressed as Kc values is shown in Fig. 3. 5.2. Calculation of the earthquake parameters During the earthquake sequence the Seismic Survey of Italy was responsible for the permanent and mobile accelerometric network that recorded the main events. The distribution of the stations all over the area is shown in Fig. 4, while the site geologic conditions are reported in Table 3. The location of the accelerometric stations is very sparse and very few stations were situated close to the epicentral area, especially for the main shock event. Nevertheless, the available accelerometric records were used for the analysis and were corrected using a method based on the Caltech Standard procedure to eliminate the dynamic effects of the transducer and the frequencies affected by signi®cant noise. Several fundamental parameters for the displacement calculation were evaluated. The Peak Ground Acceleration is the maximum absolute value of the acceleration of the entire record.

307

The Arias Intensity is equal to [3]: Ia ˆ

p Ztf 2 a …t† dt 2g 0

…9†

where a(t) is the acceleration in function of the time, and g is the acceleration of gravity. The Spectral Intensity follows, as proposed by Housner [18]: SI25 ˆ

Z2:5 0:1

PVRS…T; m† dT

…10†

where PVRS is the pseudovelocity a(t)/v m/s); v is the angular frequency (rad/s); T is the period (s) and m is the damping, set to 5%. Finally the Destructiveness Potential was proposed by Saragoni et al. [28] as follows: Pd ˆ

Ia 2n2

…11†

where Ia is the Arias Intensity and n is the number of zero crossings. In the early 1998 a large project for the evaluation of site effects was started in the Umbria±Marche area, during the earthquake sequence. Three test sites were selected: Nocera Umbra, Sellano and Fabriano, but only the data relative to the Nocera Umbra station have been used for the present analysis [33]. Table 3 Geologic site conditions of the accelerometric stations Station

Description

COLFIORITO NOCERA UMBRA

Uncertainty Weathered rock or shallow debris Soft deposits …d . 30 m and rock at the base) Uncertainty Soft deposits …d , 30 m and rock at the base) Rigid deposits …Vs . 600 m=s† Rock Weathered rock or shallow debris Rock No information Soft deposits …d , 30 m and rock at the base) Uncertainty Rock Rock Soft deposits …d , 30 m and rock at the base) Rock Rigid deposits …Vs . 600 m=s† Soft deposits …d , 30 m and rock at the base) No information No information No information

BEVAGNA MONTE FIEGNI CASTELNUOVO (ASSISI) MATELICA CASCIA FORCA CANAPINE GUBBIO LEONESSA GUBBIO (PIANA) RIETI PIETRALUNGA CAGLI PEGLIO PENNABILLI SENIGALLIA BORGO OTTOMILA Ð 2 Ð ASSISI STALLONE VALLE ATERNO AQUILPARK

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Table 4 Accelerometric parameters of the two main events recorded at Nocera Umbra (26 September 1997) Time (GMT)

Deconvolution

Component

PGA (m/s 2)

Ia (m/s)

SI (m)

Pd (ms)

0.33 0.33 9.40 9.40

No Yes No Yes

NS NS WE WE

4.8 23.6 4.3 23.8

2.4 1.4 2.6 1.67

0.785 0.725 0.785 0.777

0.01162 0.00601 0.01340 0.00613

The accelerometric station located in Nocera Umbra showed very high values of PGA, close to 0.5 g, that were interpreted as local site effects. A borehole was drilled for the project purposes close to the accelerometric site and the stratigraphic column indicated the presence of colluvial deposits of about 10 m thickness, overlaying the Marnoso Arenacea formation. A downhole test was then performed in order to obtain the S waves velocity, while the dynamic terrain properties were evaluated from laboratory tests [33]. The accelerogram at the surface was then deconvoluted using the Shake [32] program in order to obtain the accelerogram at the bedrock to be input in the analysis. The deconvolution process gave the parameters reported in Table 4 for Nocera Umbra accelerometric station. Other accelerometric stations, as it will be shown later, indicated local site effects of slight entity. Their deconvolution was therefore assumed unnecessary for the analysis purposes. The subsequent step of the earthquake parameter processing was the interpolation of the accelerometric point data. Accurate interpolation procedures, i.e. krigink that account error evaluation could not be used for the exiguous number of stations. Therefore, a moving average interpolation technique was preferred [19]. An ªInverse Distance Weightº function with decay exponent two was applied on an elliptical neighbourhood, representing the proposed source mechanism of the earthquake and the major axis of the ellipse was set to 80 km; that length means the maximum probability of using at least three points for the interpolation. The results of the interpolation procedure of the four parameters (PGA, Ia, Pd and SI25) is shown in Fig. 5 for both the NS and WE components. The two horizontal components do not show signi®cant pattern differences. It becomes clear from the interpolation results that parameters correlated to low frequencies, such as the Spectral Intensity and the Destructiveness Potential, show relative high peaks where the soft deposits are located, in correspondence of Castelnuovo and Col®orito, as shown in Fig. 5e±h.

As the relative difference between the two horizontal components is meaningless, only the NS component was used to perform the landslide prediction test. 5.3. Calculation of the displacement map with the empirical rules and test of the prediction performance The application of the seven empirical rules demand the knowledge of several parameters. It should be stressed that only the ®rst rule, proposed by Ambraseys and Srbulov [2], do not make use of the strong ground motion parameters, therefore only a map of the distance from the epicentre had to be produced and all the other parameters were easily obtained from the earthquake fundamental source parameters. Only two examples of the ®nal displacement map are shown, as the plotting scale is far too small for detailed visual comparisons (Fig. 6). The selected validation technique was the one proposed by Chung e Fabbri [9] which was ®rst applied for the comparison of probabilistic methods for landslide hazard assessment that give different probabilistic outcomes. The method, that was thought to help land planners in making decisions, states that the best hazard discrimination occurs where the smallest amount of hazard area can predict the highest number/percentage of occurred landslides. Suppose for example a very extreme situation, that is the hazard assessment indicates that all the area has the same very high hazard level. Obviously 100% of the landslides will occur in that area; a planner cannot give any indication than preventing building or infrastructure construction. If he relies on that extremely conservative result it would be inconceivable. The smaller the hazard area able to predict the highest number/percentage of landslides, the better the land use planning. The procedure is as follows: ®rst the displacement map and the landslide map, whose measured displacement is greater than 1 cm, are overlaid and the resulting table containing the unique combination is sorted on the displacement value, in descendant order, and on the landslide occurrence, in ascending order, as shown in Table 5. The third column of the table indicates the number of terrain units,

Fig. 5. Ground motion parameters: (a) Peak Ground Acceleration (10 3 m/s 2), NS component; (b) Peak Ground Acceleration (10 3 m/s 2), WE component; (c) Arias Intensity (10 3 m/s), NS component; (d) Arias Intensity (10 3 m/s), WE component; (e) Destructiveness Potential (10 5 m/s), NS component; (f) Destructiveness Potential (10 5 m/s), WE component; (g) Spectral Intensity (10 3 m), NS component; (h) Spectral Intensity (10 m), WE component. Geographical units are UTM, zone 33.

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resulting from the intersection of the two layers. Two parameters should be calculated: (a) the cumulative percentage of the hazard area, already sorted by its displacement values; (b) the cumulative percentage of the landslide area. The hazard area is plotted against the landslide area, as shown in Fig. 7a and b and the steepness of the various curves indicates the prediction accuracy. Fig. 7a has a low threshold value for the displacement, set to 1 mm. Even if that amount of displacement is not considered hazardous by various authors, it was used to verify the stability of the methods, as only the relative comparison is important. The better prediction performance is given by the method based on the Spectral Intensity parameter, but it can only predict about 80% of the total amount of landslides in about 35% of the hazard area (ratio ˆ 0.43). Other methods, like the one based on the Destructiveness Potential can predict a slightly less amount of mass movements, about 70%, in the same hazard area (ratio ˆ 0.5), but the totality of the landslides is correctly predicted in 70% of the hazard area (ratio ˆ 0.7). If a threshold is set for the Kc parameter, in terms of slope angle, the difference is negligible; in Fig. 7a and b the curve is indicated with the code CRE1. The methods based on Eqs. (3), (5), and (8) work similarly, being based on the same parameter, the Arias Intensity. The method proposed by Ambraseys and Srbulov [2] does not have a good prediction outcome, as it can only predict no more than 60% of the slides in 40% of the hazard area (ratio ˆ 0.66). In Fig. 7b the result is plotted setting a higher displacement threshold of 1 cm. Only the method proposed by Crespellani et al. [11] is stable and can successfully predict about 70% of the total amount of slides in 40% of the hazard area (ratio ˆ 0.57), while all the other methods fail, as no more than 50% of the landslides can be predicted. A further check was made on the prediction performance of the sole PGA±Kc comparison; the results are shown in Table 6. The sole PGA is not suf®cient to discriminate

hazardous areas: 91.4% of the landslides are correctly predicted in about 61% of the hazard area (ratio ˆ 0.67). In addition, the PGA value allows only a ªhard boundaryº zonation of the territory, in fact only two zones can be de®ned: hazardous or not hazardous. The other method allows us to de®ne a different degree of hazard based on the displacement thresholds or displacement values. This result encourages the use of other strong ground motion parameters in the calculation for a better prediction. 6. Discussion The results of the analysis seem encouraging for a future landslide hazard assessment of the same area and for areas showing similar failure mechanisms and terrain. Even though many assumptions were made for the geotechnical and geometric properties good results were achieved. The main problem remains the in¯uence of pore water pressure during static conditions and dynamic loading. In static conditions the water table can be brought into the Kc calculation by the m coef®cient, when using the in®nite slope analysis and a hydrological model for an area, but other methods exist, like the one proposed by Sarma [29], that is more appropriate for a single slope analysis. In dynamic condition, the role played by water in the slope stability still remains uncontrollable as the Newmark's [24] method neglects this factor. Other questionable assumption can be the constant Kc value for the entire cycling loading and the post-seismic phase. In fact it is proved that during cyclic loading the geotechnical properties of the terrain are subjected to decay, proportional to the amount of strain. Even though this factor is not considered by any empirical rule, it should be taken into account for the post-seismic stability factor calculation. The in®nite slope model can in fact be applied

Fig. 6. Examples of displacement (cm) maps according to: (a) Romeo [27]; and (b) Ambraseys and Srbulov [2]. Geographical units are UTM, zone 33.

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311

Table 5 Example of prediction performance test (Displacement ˆ landslide calculated displacement; Landslide ˆ presence/absence of landslides; Npix ˆ number of common terrain units; Cumulative area hazard ˆ cumulative percentage of the hazard area; Cumulative area landslide ˆ cumulative percentage of landslide area) Displacement

Landslide

Npix

Cumulative area hazard (%)

Cumulative area landslide (%)

126 126 125 125 124 124 123 123 122 121 121 120 120 119 119

0 1 0 1 0 1 0 1 0 0 1 0 1 0 1

155 1 170 3 164 1 177 1 185 130 2 146 2 181 1

3.91 3.91 3.93 3.93 3.95 3.95 3.97 3.97 3.99 4.00 4.00 4.02 4.02 4.04 4.04

8.22 8.26 8.26 8.33 8.33 8.36 8.36 8.38 8.38 8.38 8.43 8.43 8.47 8.47 8.50

to the slopes after the seismic excitation, introducing the changed terrain parameters. As far as the strong ground motion parameters, the PGA is not the unique useful value for seismic landslide hazard assessment, as it cannot successfully discriminate hazardous and safe areas, being a too conservative estimator. The para-

meters closely related to low frequencies, that undergo site ampli®cation in correspondence of soft terrain, represent good indicators for displacement calculations. The landslide displacement threshold of few centimetres, proposed in literature, cannot be compared to the one obtained with the empirical rules, as it represents the

Fig. 7. Comparison of different empirical rules: (a) displacement threshold ˆ 1 mm; (b) displacement threshold ˆ 10 mm. (LUZ ˆ Luzi and Pergalani; JIB ˆ Jibson et al. [20]; CRE ˆ Crespellani et al. [11]; AMB ˆ Ambraseys and Srbulov [2]; ROM ˆ Romeo [27]; LUZ1 ˆ Luzi and Pergalani; CRE1 ˆ modi®ed Crespellani et al. [11]).

312

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Table 6 Prediction performed on the PGA parameter (Npix is the number of common pixels)

[2]

Landslide

Kc # PGA

Npix

Area (%)

[3]

No Yes No Yes

No No Yes Yes

340376 365 519445 3846 869226

39.1 (tot) 8.6 (lan) 60.9 (tot) 91.4 (lan) 100

outcome of regression and it is therefore affected by standard errors. Minimal values should be considered for a matter of safety and for the model characteristics. Finally, the method that gave the best results is the one proposed by Crespellani et al. [11]. It proved to be more stable, when compared to the others, giving analogous results for different threshold values, and it also allows to neglect the PGA±Kc relative values, as high Kcs are automatically assigned very low displacement outcomes.

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7. Conclusions Several types of surface effects occurred after the 26 September 1997 earthquake in Umbria±Marche, either on bedrock, calcareous debris and sandy-clay deposits. It gave the opportunity for testing the prediction accuracy of empirical laws, based on a simple method [24], to calculate landslide displacement during a cyclic load. Shallow soil slides and debris slides were selected for the accuracy test. They were ®rst mapped on the ®eld and then by aerial photograph interpretation. Accelerometric records coming from the permanent and mobile seismic network of the Seismic Survey of Italy have been processed and interpolated to obtain strong motion parameters at each terrain unit and the possible displacements, obtained with different empirical rules, were calculated. The outcome of the various calculations have been compared to verify their prediction performances, given the real landslide occurrences. Displacement thresholds and displacement indicators were individuated to be used for future landslide hazard zonation. Although all the methods gave in general good results, the one based on the Destructiveness Potential seems to be the most accurate. It can be successfully re-applied to different areas, for evaluating soil slide and debris slide displacement for future earthquakes. References [1] Amato A, Azzara R, Basili A, Chiarabba C, Cimini GB, Cocco M, Di Bona M, Margheriti L, Mazza S, Mele F, Selvaggi G, Boschi E, Bittarelli G, Chiaraluce L, Piccinini D, Ripepe M, Courboulex F, Deschamps A, Gaffet S. Studio sismologico preliminare della

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