Abstract
The collection of relevant information about the vulnerability of infrastructure damaged by landslides is not an easy task due to the existence of several compounding factors and uncertainties. This makes it difficult to quantitatively estimate their vulnerability to slow-moving landslides. This paper presents a new vulnerability assessment model for masonry buildings on slow-moving landslides based on physical models and field observations. A masonry building model is made of brick and concrete at a scale of 1:10 to physically simulate the damage in structures caused by ground tension cracks commonly developed on slow-moving landslides. The tension crack opening process is simulated through a load-controlled table with an aperture on which the building model is constructed. The strain on the wall and its foundation were measured, and the damage of the model (crack formation and evolution for each loading step) was collected, described, and analyzed. These data were used to develop failure criteria for masonry buildings in rural areas in China in terms of a quantitative vulnerability curve. The quantitative model of vulnerability for masonry structures was established based on fuzzy mathematics and the Weibull function applied on the test data and observations. The vulnerability curve is verified with field cases of masonry buildings damaged by ground tension cracks associated with slow-moving landslides in the Three Gorges Reservoir area. The results support further testing and use of vulnerability curve proposed.
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Data availability
The model strain and damage data were from our experiment. The data of landslide deformation and building damaged were collected by the field work. And the monitoring data of the landslides in the TGR area was provided by the Hydrogeological and Environmental Geology Survey Center of China Geological Survey and Geological and Environmental Monitoring Station of Chongqing. The monitoring data is not available online. If readers want to have the data, they can request it by e-mail from the authors.
References
Bell S (1978) Successful design for subsidence. Academic Press, Large movements and structures. New York, pp 562–578
Buckingham E (1914) On physically similar systems; illustrations of the use of dimensional equations. Phys Rev 4:345–376. https://doi.org/10.1103/PhysRev.4.345
Carlà T, Macciotta R, Hendry M et al (2018) Displacement of a landslide retaining wall and application of an enhanced failure forecasting approach. Landslides 15:489–505. https://doi.org/10.1007/s10346-017-0887-7
Chen L, Cao X, Yin K et al (2016) Physical vulnerability assessment for buildings impacted by a slow moving landslide based on field work and statistical modelling. Landslides and Engineered Slopes. Theory and Practice. CRC Press, Experience 627–634
Chen Q (2022) Study on failure behavior and vulnerability of masonry structure caused by ground cracks on slow-moving landslides. Ph.D. Thesis, China Uni Geosci
Chen Q, Chen L, Gui L et al (2020) Assessment of the physical vulnerability of buildings affected by slow-moving landslides. Nat Hazards Earth Syst Sci 20:2547–2565. https://doi.org/10.5194/nhess-20-2547-2020
Chen Q, Chen L, Macciotta R et al (2022) Experimental investigation of masonry building damage caused by surface tension cracks on slow-moving landslides. SSRN Electron J. https://doi.org/10.2139/ssrn.4001115
Cruden DM, Varnes DJ (1996) Landslide types and processes. Spec Rep - Natl Res Counc Transp Res Board 247:36–75
Del Soldato M, Bianchini S, Calcaterra D et al (2017) A new approach for landslide-induced damage assessment. Geomatics, Nat Hazards Risk 8:1524–1537. https://doi.org/10.1080/19475705.2017.1347896
Deng K, Guo G, Tan Z, Yang J (2001) Analysis of movement and deformation characteristics of buildings above mining subsidence areas. J China Univ Min Technol 30:354–358
Drougkas A, Verstrynge E, Van Balen K et al (2020) Country-scale InSAR monitoring for settlement and uplift damage calculation in architectural heritage structures. Struct Health Monit 20:2317–2336. https://doi.org/10.1177/1475921720942120
Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides, Three Gorges Reservoir, China. Landslides 10:203–218. https://doi.org/10.1007/s10346-012-0326-8
Fell R, Corominas J, Bonnard C et al (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102:85–98. https://doi.org/10.1016/j.enggeo.2008.03.014
Feng SJ, Gao HY, Gao L et al (2019) Numerical modeling of interactions between a flow slide and buildings considering the destruction process. Landslides 16:1903–1919. https://doi.org/10.1007/s10346-019-01220-9
Ferlisi S, Peduto D, Gullà G et al (2015) The use of Dinsar data for the analysis of building damage induced by slow-moving landslides. In: Eng Geol Soc Terr - Volume 2: Landslide Processes. Springer 1835–1839
Galli M, Guzzetti F (2007) Landslide vulnerability criteria: a case study from Umbria, central Italy. Environ Manage 40:649–664. https://doi.org/10.1007/s00267-006-0325-4
Giardina G, Marini A, Hendriks MAN et al (2012) Experimental analysis of a masonry façade subject to tunnelling-induced settlement. Eng Struct 45:421–434. https://doi.org/10.1016/j.engstruct.2012.06.042
Grünthal G (ed) (1998) European Macroseismic Scale 1998. Cahiers du Centre Europèen de Gèodynamique et de Seismologie. Conseil de l’Europe, Conseil de l’Europe
Guo Z, Chen L, Yin K et al (2020) Quantitative risk assessment of slow-moving landslides from the viewpoint of decision-making: a case study of the Three Gorges Reservoir in China. Eng Geol 273:105667. https://doi.org/10.1016/j.enggeo.2020.105667
Huntley D, Bobrowsky P, Charbonneau F et al (2017) Innovative landslide change detection monitoring: application of space-borne InSAR techniques in the Thompson River Valley, British Columbia, Canada. In: Mikoš M, Arbanas Ž, Yin Y, Sassa K (eds) Advancing Culture of Living with Landslides. Springer International Publishing 219–229
Huntley D, Bobrowsky P, Hendry M et al (2019) Multi-technique geophysical investigation of a very slow-moving landslide near Ashcroft, British Columbia, Canada. J Environ Eng Geophys 24:87–110. https://doi.org/10.2113/JEEG24.1.87
Infante D, Di Martire D, Confuorto P et al (2019). Assessment of building behavior in slow-moving landslide-affected areas through DInSAR data and structural analysis. Eng Struct 199:109638. https://doi.org/10.1016/j.engstruct.2019.109638
Iten M, Puzrin AM, Schmid A (2008) Landslide monitoring using a road-embedded optical fiber sensor. Smart Sens Phenomena. Technol Networks Syst 6933:693315. https://doi.org/10.1117/12.774515
Jiang H, Li Y, Zhou C et al (2020) Landslide displacement prediction combining LSTM and SVR algorithms: a case study of Shengjibao landslide from the Three Gorges Reservoir area. Appl Sci 10:1–21. https://doi.org/10.3390/app10217830
Laefer DF, Hong LT, Erkal A et al (2011) Manufacturing, assembly, and testing of scaled, historic masonry for one-gravity, pseudo-static, soil-structure experiments. Constr Build Mater 25:4362–4373. https://doi.org/10.1016/j.conbuildmat.2011.03.066
Li C, Zhu J, Wang B et al (2016) Critical deformation velocity of landslides in different deformation phases. Chinese J Rock Mech Eng 35:1407–1414. https://doi.org/10.13722/j.cnki.jrme.2015.1548
Li Y, Utili S, Milledge D et al (2021) Chasing a complete understanding of the failure mechanisms and potential hazards of the slow moving Liangshuijing landslide. Eng Geol 281:105977. https://doi.org/10.1016/j.enggeo.2020.105977
Li Z, Nadim F, Huang H et al (2010) Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides 7:125–134. https://doi.org/10.1007/s10346-009-0190-3
Liang X, Gui L, Wang W et al (2021) Characterizing the development pattern of a colluvial landslide based on long-term monitoring in the Three Gorges Reservoir. Remote Sens 13:1–23. https://doi.org/10.3390/rs13020224
Liu W, Yan S, He S (2018) Landslide damage incurred to buildings: a case study of Shenzhen landslide. Eng Geol 247:69–83. https://doi.org/10.1016/j.enggeo.2018.10.025
Luo HY, Fan RL, Wang HJ, Zhang LM (2020) Physics of building vulnerability to debris flows, floods and earth flows. Eng Geol 271:105611. https://doi.org/10.1016/j.enggeo.2020.105611
Macciotta R, Carlà T, Hendry M et al (2017) The 10-mile slide and response of a retaining wall to its continuous deformation Renato. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-53487-9
Macciotta R, Hendry M, Martin CD (2016a) Developing an early warning system for a very slow landslide based on displacement monitoring. Nat Hazards 81:887–907. https://doi.org/10.1007/s11069-015-2110-2
Macciotta R, Hendry MT (2021) Remote sensing applications for landslide monitoring and investigation in western Canada. Remote Sens 13:1–23. https://doi.org/10.3390/rs13030366
Macciotta R, Martin CD, Morgenstern NR, Cruden DM (2016b) Development and application of a quantitative risk assessment to a very slow moving rock slope and potential sudden acceleration. Landslides 13:765–785. https://doi.org/10.1007/s10346-015-0609-y
Mansour MF, Morgenstern NR, Martin CD (2011) Expected damage from displacement of slow-moving slides. Landslides 8:117–131. https://doi.org/10.1007/s10346-010-0227-7
Mavrouli O, Fotopoulou S, Pitilakis K et al (2014) Vulnerability assessment for reinforced concrete buildings exposed to landslides. Bull Eng Geol Environ 73:265–289. https://doi.org/10.1007/s10064-014-0573-0
Mele A, Miano A, Di Martire D et al (2022a) Potential of remote sensing data to support the seismic safety assessment of reinforced concrete buildings affected by slow-moving landslides. Arch Civ Mech Eng 22:88. https://doi.org/10.1007/s43452-022-00407-7
Mele A, Vitiello A, Bonano M et al (2022b) On the joint exploitation of satellite DInSAR measurements and DBSCAN-based techniques for preliminary identification and ranking of critical constructions in a built environment. Remote Sens 14:1872. https://doi.org/10.3390/rs14081872
Miano A, Mele A, Calcaterra D, Di Martire D et al (2021) The use of satellite data to support the structural health monitoring in areas affected by slow-moving landslides: a potential application to reinforced concrete buildings. Struct Health Monit 20:3265–3287. https://doi.org/10.1177/1475921720983232
Ministry of Housing and Urban-Rural Development of the PRC (2010) Specification for mix proportion design of masonry mortar. China Architecture & Building Press, Beijing
Ministry of housing and Urban-rural Development of the PRC (2014) Standard for fatalness evaluation of rural area building. Construction Industry of China, Beijing
Musson RM, Grünthal G, Stucchi M (2010) The comparison of macro-seismic intensity scales. J Seismol 14:413–428. https://doi.org/10.1007/s10950-009-9172-0
Nicodemo G, Ferlisi S, Peduto D, Aceto L, Gullà G (2020) Damage to masonry buildings interacting with slow-moving landslides: a numerical analysis. In: Calvetti, F., Cotecchia, F., Galli, A., Jommi, C. (eds) Geotechnical Research for Land Protection and Development. CNRIG 2019. Lecture Notes in Civil Engineering, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-030-21359-6_6
Papathoma-Köhle M, Zischg A, Fuchs S et al (2015) Loss estimation for landslides in mountain areas e An integrated toolbox for vulnerability assessment and damage documentation. Environ Model Softw 63:156–169. https://doi.org/10.1016/j.envsoft.2014.10.003
Peduto D, Ferlisi S, Nicodemo G et al (2017) Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales. Landslides 14:1993–2007. https://doi.org/10.1007/s10346-017-0826-7
Peng S (1992) Surface subsidence engineering. Society for Mining, Metallurgy and Exploration, Colorado 77–90
Ritter S, Giardina G, Franza A, DeJong MJ (2020) Building deformation caused by tunneling: centrifuge modeling. J Geotech Geoenvironmental Eng 146:04020017. https://doi.org/10.1061/(asce)gt.1943-5606.0002223
Sangirardi M, Amorosi A, de Felice G (2020) A coupled structural and geotechnical assessment of the effects of a landslide on an ancient monastery in Central Italy. Eng Struct 225:111249. https://doi.org/10.1016/j.engstruct.2020.111249
Saito M (1965) Forecasting the time of occurrence of a slope failure, Proceedings of the 6th International Conference on Soil Mechanics and Foundation Engineering, Montreal 11:537–541
Singh A, Pal S, Kanungo DP (2019) Site-specific vulnerability assessment of buildings exposed to rockfalls. In: Renewable Energy and its Innovative Technologies. Springer 1–11
Son M, Cording EJ (2005) Estimation of building damage due to excavation-induced ground movements. J Geotech Geoenvironmental Eng 131:162–177. https://doi.org/10.1061/(asce)1090-0241(2005)131:2(162)
Sterlacchini S, Frigerio S, Giacomelli P, Brambilla M (2007) Landslide risk analysis: a multi-disciplinary methodological approach. Nat Hazards Earth Syst Sci 7:657–675. https://doi.org/10.5194/nhess-7-657-2007
Talledo DA, Miano A, Bonano M et al (2022) Satellite radar interferometry: potential and limitations for structural assessment and monitoring. J Build Eng 46:103756. https://doi.org/10.1016/j.jobe.2021.103756
Tang Y, Wu W, Yin K et al (2019) A hydro-mechanical coupled analysis of rainfall induced landslide using a hypoplastic constitutive model. Comput Geotech 112:284–292. https://doi.org/10.1016/j.compgeo.2019.04.024
Uzielli M, Nadim F, Lacasse S, Kaynia AM (2008) A conceptual framework for quantitative estimation of physical vulnerability to landslides. Eng Geol 102:251–256
Wang L, Xie M, Chai X (2014) Research on method of displacement speed ratio for spatial evaluation of landslide deformation. Rock Soil Mech 35:519–528. https://doi.org/10.16285/j.rsm.2014.02.019
Wang P, Liu Y (2021) Research and application of an improved internal thrust force measurement system for rock and soil mass based on OFDR. Geomatics, Nat Hazards Risk 12:1426–1448. https://doi.org/10.1080/19475705.2021.1927859
Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech 18:293–297. https://doi.org/10.1115/1.4010337
Wu S, Shi J, Zhang C, Wang T (2009) Preliminary discussion on technical guideline for geohazard risk assessment. Geol Bull China 28:995–1005
Xu J, Ueda K, Uzuoka R (2021) Evaluation of failure of slopes with shaking-induced cracks in response to rainfall. Landslides. https://doi.org/10.1007/s10346-021-01734-1
Xu Q, Tang M, Xu K, Huang X (2008) Research on space-time evolution laws and early warning-prediction of landslides. Chinese J Rock Mech Engnineering 27:1104–1112
Yu L, Zhou C, Wang Y et al (2022) Coupling data-and knowledge-driven methods for landslide susceptibility mapping in human-modified environments: a case study from Wanzhou County, Three Gorges Reservoir Area, China. Remote Sens 14. https://doi.org/10.3390/rs14030774
Zêzere JL, Garcia RAC, Oliveira SC, Reis E (2008) Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal). Geomorphology 94:467–495. https://doi.org/10.1016/j.geomorph.2006.10.040
Zhang J, Guo ZX, Wang D, Qian H (2016a) The quantitative estimation of the vulnerability of brick and concrete wall impacted by an experimental boulder. Nat Hazards Earth Syst Sci 16:299–309. https://doi.org/10.5194/nhess-16-299-2016
Zhang J, Zhai X, Liu B, Jiang W (2016b) Analysis and processing of strain measurement data of solid rocket motor. Meas Control Technol 35:36–39. https://doi.org/10.19708/j.ckjs.2016.09.009
Acknowledgements
We thank Xinghua Zhu, Juting Zhang, Wenfang Zhang, and other laboratory colleagues for assisting to complete the physical model test and the field investigation.
Funding
This research is supported by three projects “Studies on spatial–temporal differences in large accumulation landslide deformation and its vulnerability model for buildings in the Three Gorges Reservoir” (grant no. 41877525), “Study on quantitative vulnerability model of buildings considering the dynamic pressure integrated response mechanisms of landslide-generated impulse waves during run-up motion in reservoir area” (grant no 42207174), and “Study on the dynamic response of the quantitative vulnerability of buildings in different evolution stages of landslides” (grant no. 41601563), which are financed by the National Natural Science Foundation of China. The first author (grant no. 202106410017) is supported by the China Scholarship Council (CSC) as a visiting PhD student at the University of Alberta, Canada, under Dr. Renato Macciotta’s supervision.
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Chen, Q., Macciotta, R., Chen, L. et al. Proposed vulnerability assessment model for masonry buildings on slow-moving landslides based on physical models and field observations. Bull Eng Geol Environ 82, 371 (2023). https://doi.org/10.1007/s10064-023-03385-z
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DOI: https://doi.org/10.1007/s10064-023-03385-z