Using the Information Quantity Method to Assess the Regional Risk of Highway Slope to Improve the Level of Risk Management
Using the Information Quantity Method to Assess the Regional Risk of Highway Slope to Improve the...
Shao, Mingli;Hou, Shuai;Gao, Junfeng
2023-05-03 00:00:00
Hindawi Advances in Civil Engineering Volume 2023, Article ID 9118355, 15 pages https://doi.org/10.1155/2023/9118355 Research Article Using the Information Quantity Method to Assess the Regional Risk of Highway Slope to Improve the Level of Risk Management 1 2 3 Mingli Shao, Shuai Hou, and Junfeng Gao Zhejiang Institute of Communications Co., Ltd., Hangzhou, Zhejiang 310030, China Zhejiang Communications Construction Group Co., Ltd., Hangzhou, Zhejiang 310051, China National and Local Joint Engineering Research Center of Transportation and Civil Engineering Materials, Chongqing Jiaotong University, Chongqing 400074, China Correspondence should be addressed to Junfeng Gao; jfgao@cqjtu.edu.cn Received 14 December 2022; Revised 7 April 2023; Accepted 12 April 2023; Published 3 May 2023 Academic Editor: Fujiao Tang Copyright © 2023 Mingli Shao et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent years, there have been many studies on regional geological hazard risk assessment. However, most previous research focused on natural geological disasters such as landslides and debris fows. Tere were few special regional risk studies aimed at highway slopes infuenced deeply by artifcial factors. According to the characteristics of highway slope, based on the evaluation of regional natural infuencing factors, the artifcial infuencing factors and geological engineering information of an individual slope are introduced in this paper. Te information evaluation model, combined with the analytic hierarchy process (AHP) and ArcGIS spatial analysis, was used to evaluate the risk of disaster in the study area. Ten, the risk assessment results and risk zoning map along the highway were obtained. By comparing and analyzing the data of the management unit, the research results were in good agreement with the actual slope. Te results of this paper can efectively guide the maintenance and management of highway slopes and provide a reference for site selection of subsequent highway reconstruction and expansion projects. In general, this study can improve the level of geological risk management in highway engineering. laid a solid foundation for safe operations. However, the 1. Introduction scattered risk assessment of single highway slopes cannot Slope engineering is the most typical geological hazard in fully refect the current safety of the whole area along and highway construction and operation, which seriously afects surrounding national and provincial highways. Based on the safety of passing vehicles and pedestrians. Located on the this, it is particularly necessary to carry out a regional risk southeast coast, a mountainous, and rainy area, the geo- assessment of national and provincial highways [2]. In the logical environment of Zhejiang province, China, is very past, the information quantity method was used to evaluate complex. Under the dual action of physical and chemical regional slope risk. Te object of the evaluation was the natural slope, and the regional natural factors were selected weathering and rainfall erosion, the geotechnical properties of highway slopes continue to weaken; furthermore, high- accordingly as evaluation factors. At present, there are few way protection facilities are damaged to varying degrees, or studies on the regional risk assessment of manually exca- even completely lose their protection performance [1]. As vated highway slopes. Compared with previous research a result, a series of highway disruptions, such as collapse and results, this paper focused on the characteristics of highway landslide, occur, which pose a great threat to safe highway slopes. Te evaluation factors were selected by considering operations. natural and artifcial factors and combining the geological In recent years, the evaluation and maintenance man- engineering conditions and historical conservation data of agement of national and provincial highway slopes in every slope. Te evaluation result was more in line with Zhejiang province have had many achievements, which have reality. 2 Advances in Civil Engineering risk assessment of landslide and debris fow. In 1991, Carrara Tis paper is based on a collection of geological engi- neering information on 850 cutting slopes of fve national et al. [12] from the Department of Earth Sciences, University of Roorkee, India, applied ArcGIS method to regionalization and provincial highways in Quzhou City, Zhejiang province. Taking the 400 m bandwidth range along both sides of of landslide hazard in Ramganga Catchment area of Hi- national and provincial highways as the research area, the malayan foothills. Trough multisource data processing, regional disaster risk of national and provincial highways storage, management, and spatial superposition analysis of was studied through the information evaluation model ArcGIS, the comprehensive partition map of landslide combined with the analytic hierarchy process (AHP) and hazard was drawn. Carrara et al. [13] combined GIS tech- ArcGIS. Te research provides important scientifc reference nology with statistical model to evaluate landslide hazard. and guiding signifcance for Quzhou City to carry out slope Up to now, a variety of evaluation models have been formed maintenance and management of national and provincial for regional slope safety evaluation, including artifcial neural network, weight of evidence method [14], in- highways as well as reconstruction and expansion in later periods [3]. formation method model, multivariate statistical model, expert scoring model, grey system model, logistic regression model, nonlinear model, and fuzzy theory model [15–22]. 2. Brief Introduction of Information Method Due to the advantages of simple calculation, wide applica- Currently, risk assessment on a single slope, no matter bility, easy modeling, and high reliability, information domestic or abroad, has formed a relatively mature and method model has been widely used in scientifc research. complete theory and method. However, the regional slope Te theoretical basis of information quantity method hazard assessment is still in the stage of incomplete devel- comes from the information theory proposed by the opment. Tere is still a lack of relevant research, especially American engineer Shannon in 1948 [23]. In the application for the geological hazard assessment of highway belt region. of landslide prediction, it is considered that the occurrence In the previous achievements, many experts and scholars of landslide disaster is related to the quantity and quality of evaluated the regional geological hazard of the whole county information acquired in the process of landslide prediction, or even the whole province. Based on the information and it is measured by the amount of information. Te greater quantity model and ArcGIS spatial overlay analysis function, the amount of information, the higher the possibility of regional geological disaster susceptibility zoning map was landslide disaster. Principle of information law is trans- obtained. For example, Li et al. [4] analyzed the efect of forming the measured values of various factors of regional geological hazard susceptibility evaluation in Urumqi city. stability into information quantity. In other words, the in- Chen et al. [5] evaluated the landslide susceptibility of the formation value of each factor evaluating regional stability is distribution area of clastic rocks in Guangxi, and the used to characterize the infuence degree of each factor on landslide susceptibility zoning and prevention zoning of the regional stability [23]. Based on the single information clastic rocks in Guangxi were obtained. Wang et al. [6] quantity of the infuence factor, superposition analysis can evaluated the susceptibility of geological disasters in Chiz- get all the factors combination the total quantity of in- hou, Anhui Province. Xu et al. [7] carried out the landslide formation, which can establish an evaluation model of susceptibility regionalization and landslide susceptibility susceptibility for regional geological hazards. Te greater the evaluation in the granite distribution area of Guangxi. Sun information magnitude is, the higher the possibility of et al. [8] established a set of regional evaluation methods for geological disaster and the higher the risk degree will be [24]. high slope stability through feld geological investigation and Generally, the information magnitude can be calculated analysis based on fuzzy theoretical model. Yin and Zhu [9] according to the following formula: conducted a systematic and in-depth study on the theoretical system of spatial regionalization of landslide disaster and P Y, x x · · · x 1 2 n (1) I Y, x x · · · x � ln , 1 2 n slope instability, and carried out a study on the application of P(Y) spatial regionalization of disaster risk in the worst-hit area of where I(Y, x x · · · x ) is the information quantity to geo- landslide such as Hanjiang River basin, and achieved rela- 1 2 n logical hazards by the factor combination x x · · · x ; tively mature results. Eldeen [10] from Sweden introduced 1 2 n P(Y, x x · · · x ) is the occurrence probability of geological the research model of food risk into the study of debris fow 1 2 n disaster under the factor combination x x · · · x ; P(Y) is risk and studied the risk of landslide and debris fow disaster 1 2 n the prior probability of geological disaster. with the risk area map in 1980. In 1981, Hollingsworth and In practical application, due to the limitations of many Kovacs [11] in the United States adopted the scoring method aspects, such as the complex combination of factors and the to construct the basic framework for the risk assessment of large number of sample statistics, the application of formula landslide and debris fow and completed the landslide risk (1) to calculate the amount of information is cumbersome. assessment by means of factor stacking method, providing Terefore, the simplifed single factor information quantity methodological guidance for the risk assessment of most is usually calculated frst, and then, the total factor com- landslides and debris fow nowadays. bination information quantity is obtained by superposition Te rapid development of computer technology and GIS analysis. In practical application, the value of single factor technology provides new ideas and powerful tools for the Advances in Civil Engineering 3 (digital elevation model) data used in the study area were information can be calculated according to the following simplifed formula: derived from the geographical spatial data cloud platform (https://www.gscloud.cn/home) at a resolution of 30 × 30 m. N /N I x , H � ln , (2) Slope gradient, slope direction, and degree of surface relief S /S data were generated with the help of the ArcGIS 10.2 software analysis tools by DEM data. Quzhou City ad- where N is the number of geological disaster units of ministrative divisions, road networks, and water system data a specifc category distributed in factor x ; N is the number of were obtained from the National Earth System Science Data units in the study area containing evaluation factor x ; S is i i Center (https://www.geodata.cn/). Rainfall data were ob- the total number of units containing geological hazards in tained from 68 monitoring stations in Zhejiang province the study area; S is the total number of evaluation units in the from 1981 to 2010 by the National Meteorological In- study area. formation Center (https://data.cma.cn/), and an annual average rainfall distribution map was generated using the 3. Risk Assessment of Highway Slope Based on ArcGIS 10.2 Kriging interpolation function. Regional fault ArcGIS Information Quantity Method and rock mass type data were obtained from the OSGeo China central website (https://www.osgeo.cn/). 3.1.EvaluationMethodandUnitDivision. In the past, a large number of natural factors, DEM (digital elevation model), topographic slope, topographic aspect, topographic rough- 3.3. Selection of Evaluation Factors and Computational ness, etc., were used as evaluation factors for the assessment Analysis of Information Quantity. Most of the national and of regional geological hazard susceptibility. However, due to provincial roads in Quzhou City are mountainous, and the subjective factors, such as artifcial cutting excavation and stability of the natural slopes is largely afected by topog- the application of drainage facilities, the level of some raphy and landform. Furthermore, the annual average natural factors has changed, such as slope gradient, height, rainfall in Quzhou City is more than 1700 mm, which causes deformation, and slope stability. Terefore, artifcial exca- serious erosion and scouring efect on slopes. However, for vation protection and other factors should also be consid- the regional geological hazard assessment, randomness and ered in the regional risk assessment of highway slopes. In uncertainty of collapse and landslide disasters are often this paper, the combined action of natural and artifcial controlled by various conditions regarding the complexity of factors is considered. Firstly, using natural factors, in- the geological environment. Terefore, in this article, we formation on the 400 m regional highway bandwidth is combined the natural environmental characteristics of the calculated with the help of the information method and study area and the present slope and gave full consideration superposition of ArcGIS. Ten, the infuencing factors of the to the related data availability, the elevation, topographic artifcially excavated slope’s stability were selected as the slope, distance from fault, rock and soil type, relief degree, artifcial evaluation factors. Te combined information distance from the river, average annual rainfall, terrain slope, values of the artifcial factors in all excavated slope regions and so on. Eight factors were selected as the natural eval- were obtained using the same method. Finally, the spatial uation factors, as shown in Table 1. For the manually ex- analysis function of ArcGIS was used to superimpose and cavated slope, based on maintenance history survey and classify them, and the risk assessment results of geological engineering geological information collection, ten factors disasters were obtained for the whole region. (slope excavation angle, slope excavation height, slope de- Te division of evaluation units would directly afect the formation history and present situation, soil slope com- accuracy of evaluation results and work efciency. Terefore, pactness, soil slope moisture, rock hardness, degree of rock an evaluation unit with higher accuracy and computational slope, development degree of structure, surface of rock slope, efciency should be selected, if possible, for the ArcGIS surface and underground drainage, degree of slope pro- evaluation model. ArcGIS’s raster unit had incomparable tection) were comprehensively selected as evaluation factors. advantages over other evaluation units (grid unit and slope All of the above evaluation factors were generated in ArcGIS unit), such as simple calculation, less memory occupation, with a resolution of 30 m × 30 m by using the relevant data and higher accuracy. Terefore, the 30 m × 30 m grid unit obtained, and the Reclass tool was used to reclassify was selected as the evaluation unit in highway slope hazard according to the factor classifcation in Tables 1 and 2. Fi- evaluation. nally, the raster calculation layers of 16 evaluation factors were obtained (due to the small soil slope, the compactness 3.2.DataSourcesofEvaluationFactors. For 850 slopes along and moisture state of the soil slope were not considered in national and provincial highways in Quzhou City, the the fgure), as shown in Figures 1 and 2. geological hazard collapse and landslide information were ArcGIS was used for spatial superposition analysis on each evaluation factor grid cell layer and adverse geological obtained by collecting historical slope maintenance and geological engineering information. In the collapse and hazard point distribution layer so that the distribution of adverse geological hazard points in each evaluation factor landslide risk assessment of a 400 m bandwidth area on both sides of the national and provincial highways, the slopes with could be obtained. Tus, the value of each evaluation factor of natural and artifcially excavated slopes was calculated by potential safety hazards and those that produced adverse geological disasters are the research objects. Te DEM formula (2), as shown in Tables 1 and 2. 4 Advances in Civil Engineering Table 1: Table of risk assessment factors for natural slopes on the national and provincial highways in Quzhou City. Number of grid cells Corrected Factor Classifcation Te amount Evaluation factors amount Sorting classifcation coding of information Ni N Si S of information <150 m 1 227 65718 628 123746 −0.384731932 51 150–250 m 2 234 42233 628 123746 0.087810121 29 250–350 m 3 114 10482 628 123746 0.762229859 9 Te elevation F 350–450 m 4 27 2902 628 123746 0.606127623 14 450–550 m 5 22 1803 628 123746 0.877281420 5 Geometric >550 m 6 0 608 628 123746 — factors 0–20 1 461 106812 628 123746 −0.161981326 41 20–30 2 120 11149 628 123746 0.751832846 10 Topographic slope F 30–45 3 39 5334 628 123746 0.365151132 18 45–55 4 4 435 628 123746 0.594394520 15 >55 5 0 16 628 123746 — <500 m 1 39 6476 628 123746 0.171149522 23 500–1000 m 2 27 6545 628 123746 −0.207173622 42 1000–2000 m 3 48 9786 628 123746 −0.034060871 33 Distance from fault F 2000–3000 m 4 29 10415 628 123746 −0.600260334 52 3000–4500 m 5 70 15287 628 123746 −0.102816641 40 >4500 m 6 415 75237 628 123746 0.083326300 −0.08332630 37 Quaternary clay, argillaceous silt 1 5 3928 628 123746 −1.383001567 1.383001567 3 Cretaceous purplish red conglomerate and calcareous mudstone 2 2 1880 628 123746 −1.562433685 1.562433685 2 Cretaceous conglomerate and sandstone 3 28 12937 628 123746 −0.852196002 0.852196002 6 Jurassic mudstone, mudstone with volcanic rock 4 61 11131 628 123746 0.076830767 31 Jurassic conglomerate 5 3 1471 628 123746 −0.911639242 0.911639242 4 Triassic conglomerate, sandstone, limestone, and dolomite 6 0 1 628 123746 — — Permian limestone, mudstone, and siliceous rock 7 3 3128 628 123746 −1.666090623 1.666090623 1 Carboniferous limestone, dolomitic limestone, and conglomerate 8 0 701 628 123746 — — Devonian silty mudstone and quartz conglomerate 9 10 2283 628 123746 −0.147214363 0.147214363 25 Internal factors Te types of rock and soil Silurian sandstone, silty mudstone, occasionally sandwiched tuf 10 15 3135 628 123746 −0.058888062 0.058888062 32 F Ordovician fne sandstone, siltstone interbedded, local limestone 11 114 19928 628 123746 0.119763582 26 Ordovician mudstone and limestone 12 114 20170 628 123746 0.107693008 27 Cambrian limestone, dolomite, and argillaceous limestone 13 62 16102 628 123746 −0.276118192 0.276118192 21 Sinian dolomite, sandstone, and tuf 14 47 4848 628 123746 0.647272264 11 Proterozoic intermediate-acid volcanic rocks interbedded with 15 57 5109 628 123746 0.787738489 8 sandstone and mudstone Early Cretaceous granitic porphyry 16 0 0 628 123746 — — Late Jurassic granite, monzogranite 17 17 2637 628 123746 0.239262348 −0.239262348 44 Late Jurassic quartz syenite 18 45 12234 628 123746 −0.32186556 49 Proterozoic rhyolite porphyry and granite porphyry 19 41 2139 628 123746 1.328924548 −1.328924548 53 0–21 1 72 60209 628 123746 −1.445464813 54 21–40 2 229 32428 628 123746 0.330390668 19 Relief degree F 40–63 3 205 17476 628 123746 0.837872379 7 63–92 4 96 10254 628 123746 0.612371229 13 92–209 5 23 3708 628 123746 0.200692479 22 Advances in Civil Engineering 5 Table 1: Continued. Number of grid cells Corrected Factor Classifcation Te amount Evaluation factors amount Sorting classifcation coding of information Ni N Si S of information <50 m 1 81 9568 628 123746 0.511715869 16 50–100 m 2 87 9192 628 123746 0.623265489 12 100–200 m 3 76 16150 628 123746 −0.075495798 36 Distance from the river F 200–500 m 4 98 24448 628 123746 −0.235890023 43 500–1000 m 5 68 18918 628 123746 −0.344915233 50 >1000 m 6 218 45470 628 123746 −0.056866794 35 <1600 mm 1 36 9643 628 123746 −0.307022414 48 Te average annual 1600–1700 mm 2 128 16459 628 123746 0.426848735 17 rainfall F 1700–1800 mm 3 400 86800 628 123746 −0.096451163 39 External factors >1800 mm 4 60 10940 628 123746 0.077609676 30 Flat (−1) 1 0 162 628 123746 — — ° ° North (0–22.5 , 337.5–360 ) 2 70 15017 628 123746 −0.084996739 38 Northeast (22.5–67.5 ) 3 59 14953 628 123746 −0.251683593 47 East (67.5–112.5 ) 4 64 16028 628 123746 −0.239763198 45 Terrain slope F Southeast (112.5–157.5 ) 5 72 14755 628 123746 −0.039224978 34 South (157.5–202.5 ) 6 59 14791 628 123746 −0.240790533 46 Southwest (202.5–247.5 ) 7 94 15614 628 123746 0.170817746 24 West (247.5-292.5 ) 8 121 17145 628 123746 0.329774871 20 Northwest (292.5–337.5 ) 9 85 15281 628 123746 0.091731941 28 6 Advances in Civil Engineering Table 2: Te risk assessment factor table of artifcially excavated slopes on the national and provincial highways in Quzhou City. Number of grid cells Corrected Factor Classifcation Te amount Evaluation factors amount Sorting classifcation coding of information N N S S i i of information Soil slope ≤6 m; rock slope ≤8 m 1 31 194 258 833 −0.661796897 29 Soil slope 6–10 m; rock slope 8–15 m 2 80 298 258 833 −0.142992795 22 Slope excavation height F Soil slope 10–20 m; rock slope 15–30 m 3 124 286 258 833 0.336363812 14 Soil slope 20–40 m; rock slope 30–60 m 4 21 55 258 833 0.209263310 18 Soil slope >40 m; rock slope >60 m 5 0 0 258 833 — — Geometric factors ° ° Soil slope ≤32 ; rock slope ≤42 1 6 60 258 833 −1.130511036 32 ° ° Soil slope 32–37 ; rock slope 42–49 2 43 118 258 833 0.162589548 19 ° ° Te slope excavation angle F Soil slope 37–42 ; rock slope 49–58 3 69 161 258 833 0.324776197 15 ° ° Soil slope 42–48 ; rock slope 58–67 4 36 152 258 833 −0.268287525 26 ° ° Soil slope >48 ; rock slope >67 5 102 342 258 833 −0.037763867 21 No 1 100 657 258 833 −0.710439775 31 Slight 2 109 128 258 833 1.011391676 5 Slope deformation history and the present situation F Medium 3 47 48 258 833 1.151020648 4 Serious 4 2 29 258 833 −1.502074592 1.502074592 1 Dense 1 0 6 258 833 — — Medium dense 2 0 9 258 833 — — Te compactness of soil slope F Little dense 3 1 1 258 833 1.172074057 2 Loose 4 0 1 258 833 — — Hard 1 1 17 258 833 −1.661139287 33 Hard plastic 2 0 0 258 833 — — Soil slope moisture state F Plastic 3 0 0 258 833 — — Internal factors Soft plastic 4 0 0 258 833 — — Hard rock 1 5 31 258 833 −0.652475235 28 Relatively hard rock 2 0 0 258 833 — — Rock hardness degree of rock slope F Relatively soft rock 3 12 76 258 833 −0.673752633 30 Soft rock 4 148 584 258 833 −0.200614652 24 Extremely soft rock 5 90 125 258 833 0.843569990 8 Good 1 129 605 258 833 −0.373341996 27 General 2 93 143 258 833 0.741828920 9 Development degree of structure surface of rock slope F Relatively poor 3 31 63 258 833 0.462926535 12 Poor 4 2 5 258 833 0.255783325 16 Extremely poor 5 0 0 258 833 — — Facilities are complete 1 125 480 258 833 −0.173398309 23 Facilities are relatively complete 2 17 141 258 833 −0.943472489 0.943472489 6 Surface drainage F Facilities are not complete 3 38 84 258 833 0.378843418 13 Lack of facilities 4 76 128 258 833 0.650777134 11 Facilities are complete 1 129 376 258 833 0.102297318 20 Facilities are relatively complete 2 24 192 258 833 −0.907367484 0.907367484 7 Underground drainage F External factors Facilities are not complete 3 103 265 258 833 0.227073219 17 Lack of facilities 4 0 0 258 833 — — Good 1 181 716 258 833 −0.203109078 25 General 2 71 113 258 833 0.707366116 10 Degree of slope protection engineering diseases F Relatively poor 3 4 4 258 833 1.172074057 2 Poor 4 0 0 258 833 — — Extremely poor 5 0 0 258 833 — — Advances in Civil Engineering 7 N N 010 20 40 km 010 20 40 km The elevation Topographic slope <150 m 150-250 m 0-20° 250-350 m 20-30° 30-45° 350-450 m 45-55° 450-550 m >55° >550 m (a) (b) 40 km 010 20 Distance from fault <500 m 500-1000 m 1000-2000 m 2000-3000 m 3000-4500 m (c) (d) N N 40 km 010 20 40 km 010 20 Distance from the river Relief degree <50 m 0-21 m 50-100 m 21-40 m 100-200 m 40-63 m 200-500 m 63-92 m 500-1000 m 92-209 m (e) (f) 010 20 40 km 010 20 40 km terrain slope Flat (-1) N (0-22.5°) NE (22.5-67.5°) E (67.5-112.5°) Te average annual rainfall SE (112.5-157.5°) <1600 mm S (157.5-202.5°) SW (202.5-247.5°) 1600-1700 mm W (247.5-292.5°) 1700-1800 mm NW (292.5-337.5°) >1800 mm N (337.5-360°) (g) (h) Figure 1: Raster layer of natural slope evaluation factors: (a) the elevation, (b) topographic slope, (c) distance from fault, (d) the types of rock and soil, (e) relief degree, (f) distance from the river, (g) the average annual rainfall, and (h) terrain slope. However, from Tables 1 and 2, for part of the evaluation to the occurrence of geological disasters, but its information factors, the value was negative because of less numerous value was negative. Terefore, the values of all similar factors geological hazard points with the factor classifcation. Tis were analyzed and corrected one by one. It is appropriate for was inconsistent with the true situation, such as the severe slope risk assessment to modify the value that is benefcial to classifcation of slope deformation history. It was benefcial disaster occurrence to be positive [25]. 8 Advances in Civil Engineering (a) (b) N N 010 20 40 km 010 20 40 km Rock hardness degree Slope deformation history of rock slope and the present situation Hard rock Free Relatively soft rock Mild Soft rock Extremely sof rock Moderate (c) (d) N N 010 20 40km 010 20 40km Degree of slope protection Surface drainage engineering diseases Facilities are complete Good Facilities are relatively complete General Facilities are not complete Relatively poor Lack of facilities (e) (f) N N 010 20 40km 010 20 40km Development degree of structure surface of rock slope Good Underground drainage General Facilities are complete Relatively poor Facilities are relatively complete Poor Facilities are not complete (g) (h) Figure 2: Raster layer of evaluation factors for artifcial excavation slope: (a) slope excavation height, (b) the slope excavation angle, (c) slope deformation history and the present situation, (d) rock hardness degree of rock slope, (e) development degree of structure surface of rock slope, (f) surface drainage, (g) underground drainage, and (h) degree of slope protection engineering diseases. combination analysis, the weight of all factors should be 4. Risk Zoning of Highway Slope Based on determined frst, and analytic hierarchy process (AHP) is ArcGIS Information Quantity Method a common method. By establishing the hierarchical struc- ture model (Figure 3), constructing the judgment matrix, 4.1.AnalyticHierarchy Process(AHP)WeightDetermination. After obtaining the values of all evaluation factors, the and consistency testing, the 1–9 scale method was used to natural slope and artifcial excavated slope evaluation factors compare and score the investigated factors in pairs to cal- were combined and superimposed. For multifactor culate the weight of all factors [26]. In this paper, the expert Advances in Civil Engineering 9 Risk zoning of highway slope Geometric Internal factors External factors factors Te Te Distance Topogr- Distance Te Relief types of average Terrain from aphic from the elevation degree rock and annual slope fault slope river soil rainfall (a) Risk zoning of highway slope Geometric Internal factors External factors factors Slope Develop- Rock Degree of deformation ment Te slope Te slope hardness Under slope history degree of Surface excavation excavation degree of ground protection and the structure drainage height angle rock drainage engineering present surface of slope diseases situation rock slope (b) Figure 3: Evaluation index hierarchical structure analysis model: (a) natural slope evaluation factors and (b) evaluation factors of artifcial excavation slope. evaluation method was used to score the impact of diferent Te ArcGIS spatial analysis of Reclass tool and the levels on slope risk. Tus, the weight values of the evaluation natural discontinuous method were used to divide the factor category, criterion layer, and indicator layer were comprehensive information quantity map of the study area obtained. Finally, the fnal weight values of all evaluation into fve categories. Te default values of the discontinuity factors were obtained through multiplication, as shown in point were −0.642539, −0.255329, 0.125427, and 0.344846. Table 3. As can be seen from Table 3, for the natural study Finally, the regional slope risk map was obtained (Figure 5). area, the weight proportion of topographic relief factors was Tey were divided into basically risk-free area, mild-risk the highest, followed by topographic slope, rock, and soil area, moderate-risk area, high-risk area, and extremely high- mass type. For the artifcially excavated slope area, the risk area [27]. As shown in Figure 5 and Table 4, the areas of history and current status of slope deformation factors the above fve regions accounted for 48.35%, 22.57%, 13.10%, 10.21%, and 5.78%. Among them, the high-risk area accounted for the highest proportion, followed by the density degree of soil slope, the development degree of rock and above accounted for 16.0%, mainly distributed in the slope structural plane, slope angle, and so on. northern and southern end of the Quzhou section of the G205 national highway and the junction of Changshan County and Jiangshan City of G205 national highway and 4.2. Geological Hazard Regionalization. According to the S221 provincial highway. High-risk and extremely high-risk calculation results of single-factor information quantity in areas were mainly distributed in the high-altitude moun- Section 3, the grid calculator in ArcGIS was used to assign tainous area. Geological disasters such as collapse, slump, values to the grid images of each factor in Figures 1 and 2. landslide, and debris fow commonly occur in complex Ten, the weighted information maps of the natural research geological environments such as rivers, geological tectonic area and artifcial excavation area could be obtained by movement, terrain changes, and overlying loose stratum weighted superposition calculation in ArcGIS. Finally, the distribution, combined with abundant rainfall, which is also grid calculator was used to superposition the two, and the relatively concentrated. comprehensive information quantity map of the whole re- search area could be obtained, as shown in Figure 4. Te 5. Comparative Analysis of Slope Classification information quantity value was [−0.868412, 0.777231]. Each and Evaluation grid in the fgure represented an information attribute value, which is the infuence degree of all factors combined in the 5.1. Slope Classifcation and Evaluation Method. To further grid region on slope geological hazard. Te larger the in- improve the highway maintenance management level and formation value, the more dangerous the region, and the industrial governance capacity of Zhejiang Province, con- higher the probability of geological disasters such as collapse duct detailed maintenance, and further strengthen the slope and landslide. maintenance management of common national and 10 Advances in Civil Engineering Table 3: Evaluation factor weight value. Weight value Weight value Final weight Category of Criterion layer of criterion Index layer of indicator value of evaluation factors layer layer indicator layer Elevation F 0.1000 0.0286 Geometric factors B 0.2857 Topographic slope F 0.9000 0.2571 Distance from fault F 0.0982 0.0561 Internal factors B 0.5714 Te types of rock and soil F 0.3339 0.1908 2 4 Natural evaluation factor Relief degree F 0.5679 0.3245 Distance from the river F 0.1676 0.0239 External factors B 0.1429 Te average annual rainfall F 0.7380 0.1054 3 7 Terrain slope F 0.0944 0.0135 Slope excavation height F 0.6667 0.1343 Geometric factors D 0.2014 Slope excavation angle F 0.3333 0.0671 Slope deformation history and the present situation F 0.6000 0.4084 Te compactness of soil slop F /development degree of structure surface of rock Internal factors D 0.6806 0.3000 0.2042 Artifcial evaluation factor slope F Soil slope moisture state F /rock hardness degree of rock slope F 0.1000 0.0681 13 14 Surface drainage F 0.1373 0.0162 External factors D 0.1179 Underground drainage F 0.2395 0.0282 3 17 Degree of slope protection engineering diseases F 0.6232 0.0735 18 Advances in Civil Engineering 11 0 5 10 20 km Comprehensive information volume value High : 0.777231 Low : -0.868412 Figure 4: Comprehensive information volume map of the highway slope area along the Quzhou national and provincial highway. 0 5 10 20 km Risk zoning of geological hazards Basically risk-free area Mild risk area Moderate risk area High risk area Extremely high risk area Figure 5: Regional dangerous zoning map of highway slopes along the Quzhou national provincial highway. provincial roads, Zhejiang Provincial Highway and Trans- Combined with the practice of slope management of portation Management Center proposed the “Imple- common national and provincial roads in Zhejiang Prov- mentation Opinions on the Establishment of long-term ince, any slope angles >30 \ were classifed and evaluated Mechanism for Slope Maintenance and Classifcation according to the requirements of Table 5. Control of Common National and Provincial Roads” After obtaining the basic slope information, slope dy- (Zhejiang Gongyun (2020) No. 55) based on the pilot ex- namic information, and slope protection information, perience of Jinhua City. a comprehensive evaluation was carried out. Te specifc 12 Advances in Civil Engineering Table 4: Proportion of the area of hazardous zoning and the distribution of fle slopes. Extremely Basically Mild-risk Moderate-risk High-risk high-risk Total risk-free area area area area area Area (km ) 52.36 24.44 14.19 11.06 6.26 108.31 Area proportion (%) 48.35 22.57 13.10 10.21 5.78 100 Slope level 1 220/99.1% — — 1/0.45% 1/0.45% 222/100% (number/proportion) Slope level 2 283/49.13% 164/28.47% 4/0.70% 72/12.5% 53/9.20% 576/100% (number/proportion) Slope level 3 — 1/1.92% — 2/3.85% 49/94.23% 52/100% (number/proportion) Table 5: General national and provincial road slope classifcation evaluation system [28]. Slope classifcation and Level 1 Level 2 Level 3 Level 4 evaluation level (Stable) (Basically stable) (Local unstable) (Unstable) Engineering geological Very good Good General Very poor condition Adaptability of protection Fit Basic ft Less ft Not ft engineering Degree of hazard No or light Nonserious More severe Serious Overall stability is good, and Overall stability is poor or Overall stability is general, local has been Overall local structural damage, nonstructural damage damaged, serious structural stability is further development will Te overall evaluation exists but does not afect the diseases occur, and there is a good, and the afect the overall stability, overall stability. No impact slump, collapse, slope is stable temporarily does not afect on highway trafc overturn, and trafc operations other overall damage risks Table 6: Value of slope disease impact coefcient (SV) [29]. Impact Severity Severity description Value (SV) Roads and their structures were only slightly afected, did not afect use, and did not No or light 0.76 cause trafc disruption Roads and their structures were damaged or their functions were afected and could Nonserious still be used after timely repair. Trafc interruption, emergency repair, and 0.85 treatment time are expected to be more than 1 hour After slope failure, the highway and its structures sufered great damage or function were greatly afected, which required special reinforcement treatment to put into More severe 0.92 normal use. Trafc damage or interruption, emergency repair, and treatment time were expected to be more than 12 hours After slope failure, the highway and its structures would be completely destroyed or Serious their functions completely lost, and the trafc would be destroyed or interrupted. 1 Te time for emergency repair and treatment was expected to be more than 24 hours classifcation evaluation indexes included slope deformation SH � A × 0.36 + B × 0.11 + C × 0.28 + D × 0.2 + E × 0.05. history and current situation (A), geometric characteristics (3) (B), geological engineering conditions (C), hydrogeological conditions (D), and protection engineering status (E). Te (2) With important retaining works: slope stability index (SH) of each slope was obtained through SH � A × 0.36 + B × 0.05 + C × 0.2 + D × 0.11 + E × 0.28, a weighted calculation based on the comprehensive analysis and evaluation of the investigation status. Te calculation (4) formula was determined as follows: where A, B, C, D, E are scoring values of each (1) Simple slope protection project: indicator. Advances in Civil Engineering 13 Table 7: Te slope classifcation and evaluation level division. Slope evaluation composite SRI < 25 25 ≤ SRI < 50 50 ≤ SRI < 75 SRI ≥ 75 index (SRI) Level 1 Level 2 Level 3 Level 4 Slope classifcation and evaluation level Stable Basically stable Local unstable Unstable 0 (0%) 52 (6%) 222 (26%) 576 (68%) Slope level 1 Slope level 3 Slope level 2 Slope level 4 Figure 6: Te number and proportion of slope classifcation along the national and provincial roads in Quzhou City. 118°20′0″E 118°45′0″E 119°10′0″E 29°30′0″N 29°15′0″N 29°0′0″N 28°45′0″N 0 5 10 20 km Slope level 1 28°30′0″N Slope level 2 Slope level 3 Study area National and provincial highway Fault River 28°15′0″N Figure 7: Te distribution of fled slopes along the national provincial highway in Quzhou City. 14 Advances in Civil Engineering Finally, the infuence degree and infuence range of slope (2) Combined with the analytic hierarchy process disease on highway trafc and surrounding structures were (AHP) and ArcGIS spatial analysis model evaluation, the risk zoning map of banded areas along the comprehensively considered to obtain the highway slope evaluation composite index (SRI), which was calculated by highway was obtained. It not only points out the the following formula (slope disease infuence coefcient direction for highway slope maintenance manage- (SV) value is shown in Table 6): ment but also further improves management ef- ciency and reduces maintenance costs. SRI � SH × SV. (5) (3) Te regional risk division obtained in this paper based on the research of Quzhou City national and According to the highway slope evaluation composite index (SRI), the slopes were divided into four levels, provincial road slopes was more consistent with the slope classifcation evaluation results and the actual according to Table 7. slopes in Zhejiang Province, China. It can be used as the basic research method for slope management. 5.2. Results of Slope Classifcation and Evaluation. After In the future, during regional risk assessments, evalua- collecting historical data, the geological engineering survey, tion factors can be selected specifcally based on the research and the analysis from 2020 to 2021, classifcation and objects. Meanwhile, the feld collection of research objects evaluation of 850 slopes on fve national and provincial should be strengthened. Tis can greatly improve the ac- highways (including G205, G320, G351, G528, and S221) in curacy of the research results and has more practical sig- Quzhou City were completed. Te specifc classifcation and nifcance for project management. its proportion are shown in Figures 6. Figure 7 shows the distribution of various slopes along national and provincial Data Availability roads in Quzhou City. Te data supporting the current study are available from the corresponding author upon request. 5.3. Comparative Analysis of Classifcation Evaluation and Regional Risk Regionalization. Trough the ArcGIS spatial Conflicts of Interest analysis and extraction tool, the distribution of various types of highway slopes in the regional hazard zoning map was Te authors declare that they have no conficts of interest. obtained statistically, as shown in Figure 5 and Table 4 in Section 4.2. Acknowledgments As can be seen from Table 4, 99.1% of level 1 slopes were distributed in basically risk-free areas, 78.3% of level 2 slopes Te research was performed as part of the employment of were distributed in moderate or lower risk areas, and 98.08% the authors at Zhejiang Institute of Communications Co., of level 3 slopes were distributed in high or above risk areas. Ltd., Zhejiang Communications Construction Group Co., Te above conclusions indicate that the results of highway Ltd., and Chongqing Jiaotong University. slope regional risk zoning were consistent with the slope classifcation evaluation. References 6. Conclusion [1] C. Yang, Wangkai, Y. Cao, and W. Shu, “Research on slope disease investigation and treatment measures of Zhejiang In order to further improve the maintenance and man- Expressway,” Highways, vol. 64, no. 03, pp. 259–265, 2019. agement of highway slopes, this study took Quzhou City’s [2] A. Yacine, S. Zahra, T. Rania et al., “Assessing landslide susceptibility using a machine learning-based approach to national and provincial roads as an example to conduct achieving land degradation neutrality,” Environmental Earth regional risk assessment research on highway slopes. Sciences, vol. 80, no. 575, 2021. Combined with the characteristics of highway slopes and [3] B. Prasad Pandey and K. Raj Kafe, Landslide Hazard As- considering the natural factors, artifcial factors, and geo- sessment Using Uav Imagery And Gis For Road Planning And logical engineering information of individual slopes, the Development In Chure Area: Sindhuli-Hetauda Section, Nepal regional risk zoning map of highway slopes was obtained. Engineering College, Bhaktapur, Nepal, 2020. Te prone areas of geological disasters were defned, which [4] C. Li, Y. Song, and X. Qi, “Efect analysis of geological hazard provides a basis for the decision of highway slope mainte- susceptibility assessment based on ArcGIS information nance management. quantity model in Urumqi city,” Xinjiang Geology, vol. 37, no. 01, pp. 34–39, 2019. (1) Compared with previous research methods, this [5] L. Chen, Y. Jiang, W. Chuanjian, and Y. Xu, “Landslide study combined artifcial infuencing factors and susceptibility evaluation based on ArcGIS and information individual geological slope engineering information quantity method,” Journal of Guangxi University(Natural on the basis of considering natural infuencing Science Edition), vol. 41, no. 01, pp. 141–148, 2016. factors. Te evaluation results were more accurate [6] L. Wang, J. Wu, B. Zhao, Z. Yao, and L. Zhang, “Assessment of and reliable. geological hazard susceptibility in Chizhou, Anhui Province Advances in Civil Engineering 15 based on GIS and information quantity model,” Chinese [23] C. E. Shannon, “A mathematical theory of communication,” Journal of Geological Hazard and Prevention, vol. 31, no. 03, John Wiley and Sons, Ltd, vol. 27, no. 4, 1948. pp. 96–103, 2020. [24] J. Yu, A. Sun, and G. Zhang, “Quantitative study on landslide risk regionalization based on GIS technology,” Railway En- [7] Y. Xu, Y. Lu, D. Li, and L. Chen, “Evaluation of landslide susceptibility of granite distribution area in Guangxi based on gineering Cost Management, vol. 30, no. 1, pp. 14–19, 2015. GIS and information quantity model,” Journal of Engineering [25] Q. Meng, W. Sun, and T. Wang, “Evaluation of geological Geology, vol. 24, no. 4, pp. 693–703, 2016. hazard susceptibility in fengxian county, shaanxi province,” [8] S. Sun, B. Zhu, and H. Ma, “A regional high slope stability Journal of Engineering Geology, vol. 19, no. 3, pp. 388–396, evaluation method based on fuzzy theory,” Journal of the 2011. China Railway Society, vol. 32, no. 3, pp. 77–83, 2010. [26] L. Lu, B. Zhang, and D. Zheng, “Risk assessment model of [9] K. Yin and L. Zhu, “Study on spatial zoning and GIS ap- highway soil cutting slope based on AHP-CRITIC,” [J]. plication of landslide disaster,” Earth Science Frontiers, no. 02, Yangtze River.vol. 54, no. 01, pp. 133–139, 2023. pp. 279–284, 2001. [27] J. R. Muhammad, F. F. Nabilah, N. S. Aisyah et al., “Landslide [10] M. T. Eldeen, “Predisaster physical planning: integration of susceptibility mapping using geographic information system disaster risk analysis into physical planning - a case study in (GIS) in kuala balah, jeli, kelantan,” IOP Conference Series: Tunisia,” Disasters, vol. 4, no. 2, pp. 211–222, 1980. Earth and Environmental Science, vol. 1102, pp. 12–48, 2022. [28] M. Shao, Y. Yang, H. Hu, T. Kang, and H. Chen, “Research [11] R. Hollingsworth and G. S. Kovacs, “Soil slumps and debris fows: prediction and protection,” Environmental and Engi- and application of highway slope engineering geological in- neering Geoscience, vol. 18, no. 1, pp. 17–28, 1981. formation system,” China and Foreign Highway, vol. 41, no. 6, [12] A. Carrara, M. Cardinali, R. Detti, F. Guzzetti, V. Pasqui, and pp. 51–54, 2021. P. Reichenbach, “GIS techniques and statistical models in [29] Peoples Publishing House, Technical Specifcation for Risk evaluating landslide hazard,” Earth Surface Processes and Assessment of In-Service Highway Slope Engineering, People’s Landforms, vol. 16, no. 5, pp. 427–445, 1991. Communications Publishing House Co Ltd, Beijing, China, [13] A. Carrara, M. Cardinali, and F. Guzzetti, Uncertainty in 2019. Assessing Landslide hazard and Risk, Springer, Manhattan, NY, USA, 1992. [14] M. Shang, R. Ma, Y. Zhang, and Y. Liu, “Analysis of collapse sensitivity based on GIS-based evidence weight method,” Journal of Engineering Geology, vol. 26, no. 5, pp. 1211–1218, [15] K. Yin, “Classifcation of landslide disaster prediction and forecast,” Chinese Journal of Geological Hazards and Pre- vention, vol. 14, no. 4, pp. 12–18, 2003. [16] Q. Fan, N. Ju, X. Xiang, and J. Huang, “Application of weight of evidence method in regional landslide hazard assessment: a Case study of Guizhou Province,” Journal of Engineering Geology, no. 3, pp. 474–481, 2014. [17] Z. Fan, X. Gou, M. Qin, Q. Fan, J. Yu, and J. Zhao, “Evaluation of geological hazard susceptibility based on information quantity model and Logistic regression model,” Journal of Engineering Geology, vol. 26, no. 2, pp. 340–347, 2018. [18] L. Fan, R. Hu, F. Zeng, S. Wang, and X. Zhang, “Application of weighted information information model in landslide sus- ceptibility assessment: a case study of enshi city, hubei province,” Journal of Engineering Geology, vol. 20, no. 4, pp. 508–513, 2012. [19] O. F. Althuwaynee, B. Pradhan, H.-J. Park, and J. H. Lee, “A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide sus- ceptibility mapping,” Catena, vol. 114, pp. 21–36, 2014. [20] H. J. Park, J. H. Lee, and I. Woo, “Assessment of rainfall- induced shallow landslide susceptibility using a GIS-based probabilistic approach,” Engineering Geology, vol. 161, pp. 1– 15, 2013. [21] V. B. Che, M. Kervyn, C. E. Suh et al., “Landslide susceptibility assessment in Limbe (SW Cameroon): a feld calibrated seed cell and information value method,” Catena, vol. 92, pp. 83–98, 2012. [22] H. Shahabi, S. Khezri, B. B. Ahmad, and M. Hashim, “RETRACTED: landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models,” Catena, vol. 115, pp. 55–70, 2014.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png
Advances in Civil Engineering
Hindawi Publishing Corporation
http://www.deepdyve.com/lp/hindawi-publishing-corporation/using-the-information-quantity-method-to-assess-the-regional-risk-of-UNCaT5wKZ8