Comprehensive evaluation method for pavement performance of pavement-free pavement

Abstract

The invention discloses a pavement performance comprehensive evaluation method for a pavement-free pavement, and belongs to the technical field of pavement performance evaluation. The pavement performance comprehensive evaluation method comprises the following steps: S1, setting pavement performance influence factors; s2, establishing an index analytic hierarchy structure model and determining a grading standard; s3, determining the weight of each evaluation index; s4, comprehensive weight evaluation is determined, a total evaluation score is obtained, and a relation is established according to the total evaluation score and the evaluation grade; according to the comprehensive evaluation method for the pavement performance of the pavement-free pavement, provided by the invention, the index hierarchical structure model containing a plurality of evaluation indexes is established, the grading standard is determined, the weight of each evaluation index is determined, and the comprehensive weight evaluation and grading methods are adopted, so that the pavement performance of the pavement-free pavement is evaluated. And a comprehensive and accurate evaluation system is provided for the pavement performance of the pavement-free pavement.

Description

Technical Field

[0001] The invention relates to the technical field of road performance evaluation, and in particular to a comprehensive evaluation method for road performance of an unpaved road surface. Background Art

[0002] The development of airport pavement design standards has gone through several important stages. Initially, in the late 1920s, the basic methods for highway subgrade and pavement design were first proposed. Subsequently, in the 1940s and 1950s, the United States developed design standards for asphalt concrete flexible airport pavements based on the California Bearing Ratio (CBR), and this practice was followed by other countries. In the 1960s, the United States developed the ASSTHO design method, which matured in the early 1990s and the empirical method was improved. In recent years, many countries, including China, have begun to reformulate pavement design specifications based on the mechanical-empirical method.

[0003] Flexible pavement has become one of the main pavement forms studied internationally due to its good shock absorption performance, suitability for mechanized construction, short construction period and easy maintenance. Between 1928 and 1929, American California engineer Porter.OJ and others identified three main factors leading to pavement damage through research and proposed the California Bearing Ratio (CBR) to measure the shear resistance of the roadbed. CBR is an indicator for evaluating the bearing performance of soil subgrade materials, which represents the ratio of the material's ability to resist compression deformation under local load to the pressure of standard crushed stone at the same depth.

[0004] In the following decade, Porter.OJ et al. recorded a large amount of data on pavement damage, CBR values, and usage intensity, analyzed the relationship between these data, and formed an empirical method. This method provides a reference for the compaction of road subgrade, but it is mainly based on the summary of historical empirical data and is not analyzed through mechanical theory. In the 1940s, the U.S. Army Corps of Engineers (USACE) began relevant research in order to build runways that can withstand the takeoff and landing of large aircraft, and introduced empirical methods into the airport runway construction standards.

[0005] Since the 1950s, the empirical method has begun to develop into a calculation formula. In 1956, USACE summarized the previous experience of accelerated loading and proposed an empirical formula for the relationship between the equivalent single wheel of an aircraft and the pavement thickness, which was promoted and used in various states in the United States. However, since the empirical method is based on the actual use of airport runways and real flight tests, as the conditions of airport use change, this method gradually exposes more and more problems and shortcomings.

[0006] In order to solve these problems, from the 1960s to the 1970s, the mechanical-empirical method began to be applied to airport pavement design. The mechanical-empirical method establishes a pavement structure model, converts loads and environmental factors into representative values ​​or equivalent values, and then calculates based on the structure model to obtain the quantitative relationship between pavement response and empirical failure conditions, thereby obtaining regular design conclusions.

[0007] Entering the 21st century, with the advancement of technology, the U.S. Army Engineering Research and Development Center (ERDC) began research on the "Joint Rapid Airfield Construction (JRAC)" project in 2001. One of the important research contents was "simple pavement of field airfields that can ensure the completion of combat missions." The smooth progress of these exercises marked the success of the JRAC project, and its results have been used to this day.

[0008] China started research on flexible pavements relatively late, with the early focus on flexible paved pavements, while research on soil pavements at field airports was relatively limited. In recent years, China has also begun to re-formulate pavement design specifications based on the mechanics-empirical method to meet actual needs.

[0009] Based on the above, there is a relative lack of research on unpaved pavements at home and abroad, and there is no complete design specification for unpaved pavements. This method aims to solve the problems existing in the comprehensive evaluation of the pavement performance of unpaved pavements. By establishing an indicator hierarchy model containing multiple evaluation indicators and determining the grading standards, determining the weights of each evaluation indicator, and the comprehensive weight evaluation and grade division method, a comprehensive and accurate evaluation system for the pavement performance of unpaved pavements is provided to fill this gap. Summary of the invention

[0010] The purpose of the present invention is to provide a comprehensive evaluation method for the road performance of an unpaved road surface to solve the problems existing in the above-mentioned background technology.

[0011] To achieve the above object, the present invention provides a method for comprehensively evaluating the road performance of an unpaved road surface, comprising the following steps:

[0012] S1. Set factors affecting road performance;

[0013] S2. Establish an indicator hierarchy model and determine the grading standards;

[0014] S3. Determine the weight of each evaluation index;

[0015] S4. Determine the comprehensive weight evaluation and obtain the total evaluation score, and establish a relationship between the total evaluation score and the evaluation level.

[0016] Preferably, the indicator hierarchy model of step S2 includes five first-level indicators, namely, road environment and humidity condition index E, road surface flatness index F, road surface damage degree index D, road surface strength index S and road surface stiffness index M, and each first-level indicator contains several second-level indicators, and four grading standards are determined according to the second-level indicators.

[0017] Preferably, the environment and humidity condition index E includes two secondary standards: climate factor E1 and humidity E2:

[0018] The three indicators of the hottest month average maximum temperature, annual extreme minimum temperature and annual precipitation in climate factor E1 comprehensively and quantitatively reflect the climate characteristics of a certain place and are divided into four levels; the lower the level, the more serious the impact of the climate factor;

[0019] Humidity E2: In the same region, under the same natural conditions, the strength of the soil base is largely related to its relative water content. The strength of the soil base in a slightly moist state is higher, and vice versa.

[0020] When conducting on-site investigation, for the original road surface, the average consistency of the soil within 80cm below the bottom of the road trench in unfavorable seasons is used to determine it. Within 80cm below the bottom of the road trench, soil samples are taken every 10cm to measure the natural moisture content, plastic moisture content and liquid limit moisture content of the soil foundation, and the moisture content is calculated according to the following formula. When on-site investigation is not possible, the critical height of the soil foundation can be used as the judgment standard, and the calculation formula is:

[0021]

[0022] Among them, ω c Indicates the average consistency of the soil; ω L represents the liquid limit of soil; ω p represents the plastic limit of soil; ω represents the average water content of soil;

[0023]

[0024]

[0025] Among them, ω i It indicates the natural water content of the i-th layer of soil within 80cm below the bottom of the trench, with each layer consisting of 10cm; ω ci represents the density of the i-th layer; ω Li Indicates the liquid limit water content of the same layer of soil (76g counterweight); ω pi Indicates the limit water content of the same layer of soil; It indicates the arithmetic mean consistency of the soil within 80cm below the bottom of the trench; According to the relationship with the decomposition consistency, it is divided into four grading standards: dry, medium-wet, moist and over-wet.

[0026] Preferably, the road surface flatness index F includes:

[0027] The calculation formula of the roughness index IRI is:

[0028]

[0029] Among them, Δ yi Indicates the elevation difference between adjacent measuring points; Δ xi Indicates the distance between adjacent measurement points; L indicates the total length of the measurement; z indicates the total number of measurement points;

[0030] Gap average value K cp , refers to the average distance between the road surface and an ideal plane. This distance can be an upward or downward deviation and is the ratio of the total number of measuring points. The calculation formula is:

[0031]

[0032] Among them, R i represents the gap value; a represents the number of gaps;

[0033] The percentage of gaps greater than 5 mm, δ, refers to the percentage of measurement points with gaps greater than 5 mm in the total number of points. The calculation formula is:

[0034]

[0035] in, Indicates the number of measuring points with a gap greater than 5 mm.

[0036] According to IRI, K cp The road surface roughness is divided into four classification standards: very smooth, relatively smooth, rugged and very rugged.

[0037] Preferably, the road surface damage index D includes:

[0038] The crack area ratio C is the ratio of the converted pavement crack area to the total area of ​​the tested road section, and is divided into four grading standards: excellent, good, medium and poor;

[0039] Maximum wheel track depth H max , the calculation process is as follows:

[0040]

[0041]

[0042] Among them, σ min Indicates the minimum bearing capacity coefficient; P K Indicates the mass of a single wheel; B indicates the width of the rigid wheel; D indicates the wheel diameter; represents relative thrust; α represents conversion factor;

[0043] In order to ensure the taxiing safety of aircraft on dirt runways, the maximum wheel track depth is usually limited to:

[0044] H max <0.07D;

[0045] The minimum load-bearing coefficient is the minimum value of the tire pressure that the pavement structure can withstand without damage, which is:

[0046]

[0047] According to H max and σ min It is divided into four classification standards: very safe, generally safe, not very safe and extremely unsafe.

[0048] Preferably, the road surface strength index S includes:

[0049] Unconfined compressive strength index q u , unconfined compressive strength refers to the ultimate strength of the specimen to resist axial pressure in the absence of lateral pressure. The unconfined compressive strength usually takes the maximum axial stress as the unconfined compressive strength of the specimen. If the maximum axial stress is not obvious, the stress at 15% axial strain can be taken as the unconfined compressive strength of the specimen. The test results are usually expressed by drawing a stress-strain curve, with the maximum axial stress as the value of the unconfined compressive strength, and divided into four classification standards;

[0050] Shear strength index c, It refers to the index of soil foundation strength and resistance to deformation, and is also divided into four classification standards;

[0051] California Bearing Ratio CBR, through DCP test, establishes the relationship between DCP test penetration rate PR and CBR as follows:

[0052]

[0053] It is divided into four grading standards: high load-bearing capacity, good load-bearing capacity, medium load-bearing capacity and low load-bearing capacity.

[0054] Preferably, the road surface stiffness index M includes:

[0055] Rebound modulus E r , refers to the ratio of the deformation of the soil under load to the deformation recovered after the load is removed, and is an indicator of the elastic characteristics of the soil, as follows:

[0056]

[0057] Among them, σ v represents vertical stress; ε vIndicates vertical strain; divided into four grading standards: high modulus, medium modulus, low modulus and extra-low modulus;

[0058] The reaction modulus K is the proportional constant between pressure and deflection value, as follows:

[0059]

[0060] Among them, P represents unit pressure; l represents the deflection value, and it is divided into four classification standards.

[0061] Preferably, step S3 includes determining the weight coefficient of the indicator by AHP method, the steps are as follows:

[0062] S31. Decompose multi-objective decision-making problems and establish a hierarchical structure;

[0063] S32, constructing a judgment matrix A;

[0064] S33, find the maximum eigenvalue λ of the judgment matrix A max , normalize its eigenvector W and obtain the weight of each factor:

[0065] AW=λ max W;

[0066] S34, perform consistency check, the formula is as follows:

[0067]

[0068] Where n represents the order of the judgment matrix A; CR represents the random consistency ratio; CI represents the consistency index; RI represents the average random consistency index;

[0069] Through the above steps, the first-level indicator judgment matrix, the second-level indicator judgment matrix and the evaluation indicator weight distribution table are obtained.

[0070] Preferably, step S4 specifically includes:

[0071] S41. Construct an expert questionnaire, including basic information and the scoring of road performance influencing factors, and the questionnaire scores are divided into four levels;

[0072] S42. Determine the indicator weights, including:

[0073] S421. Calculate the average score of each indicator to ensure that the score of each indicator reflects the overall trend. The formula is:

[0074]

[0075] Where N is the total number of indicators;

[0076] S422. Standardization is the process of adjusting data to make the comparison between different indicators fairer. Common standardization methods include Z-score standardization, which standardizes data by subtracting the mean and dividing by the standard deviation;

[0077] S423, normalization processing, the weight calculation formula is:

[0078]

[0079] S424. Verify the rationality of the weights, conduct sensitivity analysis and expert review. Sensitivity analysis ensures the robustness of weight allocation by checking the impact of weights on the final results. At the same time, expert review provides professional judgment to ensure that weight allocation complies with industry standards and professional practices;

[0080] S43. Calculate the comprehensive weight evaluation and establish a relationship with the evaluation level.

[0081] Therefore, the present invention adopts the above-mentioned comprehensive evaluation method for the road performance of unpaved pavements, by establishing an indicator hierarchy model including multiple evaluation indicators and determining the grading standards, determining the weight of each evaluation indicator, and the comprehensive weight evaluation and grade classification method, to provide a comprehensive and accurate evaluation system for the road performance of unpaved pavements.

[0082] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of a comprehensive evaluation method of road performance of an unpaved road surface according to the present invention;

[0084] Figure 2 It is a hierarchical analysis structure diagram for evaluating the road performance of an unpaved road surface according to the present invention. DETAILED DESCRIPTION

[0085] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] like Figure 1 As shown, a comprehensive evaluation method for road performance of an unpaved road surface comprises the following steps:

[0087] S1. Set factors affecting road performance;

[0088] 1. Environmental factors

[0089] The climate zoning of pavement performance is a regional division based on the impact of climate conditions on pavement performance, which helps to select materials suitable for the local climate and design appropriate pavement structures.

[0090] 2. Road surface smoothness

[0091] Road surface smoothness is one of the important indicators to measure road performance, which directly affects driving stability and comfort.

[0092] 3. Road damage

[0093] 4. Pavement structure bearing capacity

[0094] S2. Establish an indicator hierarchy model and determine the grading standards;

[0095] According to the selection process of various indicators, a tomographic analysis structure diagram of road performance evaluation of unpaved pavement can be established, such as Figure 2 shown.

[0096] Establishment of grading standards

[0097] 1. Road environment and humidity index E

[0098] The water temperature condition of the roadbed refers to the combined effect of humidity and temperature changes on the roadbed. The volume of the roadbed soil and pavement materials expands and contracts with the rise and fall of temperature and humidity in the roadbed and pavement structure. Since temperature and humidity change with the environment and are unevenly distributed along the depth of the structure, the changes in expansion and contraction are different at different times and depths, which will have a certain impact on the strength of the roadbed.

[0099] (1) Climate factor E1

[0100] The temperature of the atmosphere changes periodically throughout the year and throughout the day and night. Atmospheric precipitation will penetrate into the roadbed through the road surface, shoulder slopes and side ditches. The classification is based on annual rainfall and is shown in Table 1.

[0101] Table 1 Classification

[0102] grade Average maximum temperature of the hottest month Annual extreme minimum temperature Annual rainfall classification A ＜20℃ ＞-9℃ <250mm B 20℃～30℃ -21℃～-9℃ 250mm~500mm C 30℃～40℃ -37℃～-21.5℃ 500mm~1000mm D ＞40℃ ＜-37℃ >1000mm

[0103] The three indicators comprehensively and quantitatively reflect the climate characteristics of a certain place. The lower the level, the more serious the impact of climate factors.

[0104] (2) Humidity E2

[0105] In the same region, under the same natural conditions, the strength of the soil base is largely related to its relative water content. The strength of the soil base in a slightly moist state is higher, and vice versa.

[0106] When conducting on-site investigation, for the original road surface, the average consistency of the soil within 80cm below the bottom of the road trench in unfavorable seasons is used for determination. Within 80cm below the bottom of the road trench, soil samples are taken every 10cm to determine the natural water content, plastic water content and liquid limit water content of the soil foundation, and the water content is calculated according to the following formula. When on-site investigation is not possible, the critical height of the soil foundation can be used as a judgment standard.

[0107]

[0108] Among them, ω c Indicates the average consistency of the soil; ω L represents the liquid limit of soil; ω p represents the plastic limit of soil; ω represents the average water content of soil;

[0109]

[0110]

[0111] Among them, ω i It indicates the natural water content of the i-th layer of soil within 80cm below the bottom of the trench, with each layer consisting of 10cm; ω ci represents the density of the i-th layer; ω Li Indicates the liquid limit water content of the same layer of soil (76g counterweight); ω pi Indicates the limit water content of the same layer of soil; It represents the arithmetic average consistency of the soil within 80cm below the bottom of the trench; its grade classification is shown in Table 2.

[0112] Table 2 Classification

[0113]

[0114] Note: 1. H0 is the height of the top surface of the soil foundation from the groundwater or surface water level in unfavorable seasons;

[0115] 2. Surface waterlogging refers to waterlogging for more than 20 days in unfavorable seasons.

[0116] 2. Road surface smoothness index F

[0117] (1) Flatness index IRI (F1)

[0118] The roughness index is a measure used to assess the smoothness of a road or other surface.

[0119]

[0120] Among them, Δ yi Indicates the elevation difference between adjacent measuring points; Δ xi It represents the distance between adjacent measuring points; L represents the total length of measurement; z represents the total number of measuring points; its classification is shown in Table 3.

[0121] Table 3 Classification

[0122]

[0123] (2) Gap average value K cp (F2)

[0124] The average gap value refers to the average distance between the road surface and an ideal plane. This distance can be an upward or downward deviation and is the ratio of the total number of measuring points.

[0125]

[0126] Among them, R i represents the gap value; a represents the number of gaps; and the grade classification is shown in Table 4.

[0127] Table 4 Classification

[0128]

[0129] (3) Percentage of gaps greater than 5 mm (F3)

[0130] The percentage of gaps greater than 5 mm, δ, refers to the percentage of measurement points with gaps greater than 5 mm in the total number of points. The calculation formula is:

[0131]

[0132] in, Indicates the number of measuring points with a gap greater than 5 mm.

[0133] Table 5 Classification

[0134]

[0135] 3. Road damage index D

[0136] (1) Crack area ratio C(D1)

[0137] The ratio of the converted pavement crack area to the total area of ​​the tested road section is classified into different levels as shown in Table 6.

[0138] Table 6 Classification

[0139]

[0140] (2) Maximum wheel track depth H max (D2)

[0141] When an aircraft taxis on a dirt runway, the wheels will produce deep ruts. If the rut depth is too large, it will make the aircraft taxiing unsafe.

[0142] H = h1-h2;

[0143] Among them, H represents the wheel track depth; h1 represents the depth of the wheel pressed into the soil; h2 represents the depth of the soil body recovering from deformation;

[0144]

[0145]

[0146] Among them, σ min Indicates the minimum bearing capacity coefficient; P K Indicates the mass of a single wheel; B indicates the width of the rigid wheel; D indicates the wheel diameter; represents relative thrust; α represents conversion factor;

[0147] In order to ensure the taxiing safety of aircraft on dirt runways, the maximum wheel track depth is usually limited to:

[0148] H max <0.07D;

[0149] The minimum bearing capacity coefficient of soil is the minimum value of tire pressure that the pavement structure can withstand without damage, and the requirement is:

[0150]

[0151] The classification is shown in Table 7:

[0152] Table 7 Classification

[0153]

[0154] 4. Pavement strength index S

[0155] (1) Unconfined compressive strength index q u (S1)

[0156] Unconfined compressive strength refers to the ultimate strength of a specimen to resist axial pressure in the absence of lateral pressure. The unconfined compressive strength usually takes the maximum axial stress as the unconfined compressive strength of the specimen. If the maximum axial stress is not obvious, the stress at 15% of the axial strain can be taken as the unconfined compressive strength of the specimen. The test results are usually expressed by drawing a stress-strain curve, with the maximum axial stress as the value of the unconfined compressive strength. The grade classification is shown in Table 8.

[0157] Table 8 Classification

[0158]

[0159] (2) Shear strength index c,

[0160] The shear strength index refers to the index of soil base strength and deformation resistance, and its grade classification is shown in Table 9.

[0161] Table 9 Classification

[0162]

[0163] (3) California Bearing Ratio CBR, unit is % (S3)

[0164] Through the DCP test, the relationship between the penetration rate PR and CBR of the DCP test is established as follows:

[0165]

[0166] The unit of PR is mm / hammer blows, and the grade classification is shown in Table 10.

[0167] Table 10 Classification

[0168]

[0169] 5. Pavement stiffness index M

[0170] (1) Rebound modulus E r (M1)

[0171] The modulus of resilience usually refers to the ratio of the deformation of the soil under load to the deformation after the load is removed. It is an indicator of the elastic properties of the soil. The modulus of resilience of soil samples is determined through indoor tests.

[0172]

[0173] Among them, σ v represents vertical stress; ε v Indicates vertical strain; the grade classification is shown in Table 11.

[0174] Table 11 Classification

[0175]

[0176] (2) Reaction modulus K (M2)

[0177] The reaction modulus is the proportionality constant between pressure and deflection value, as follows:

[0178]

[0179] Wherein, P represents unit pressure; l represents deflection value, and the grade classification is shown in Table 12.

[0180] Table 12 Classification

[0181]

[0182] S3. Determine the weight of each evaluation index;

[0183] The steps to determine the weight coefficient of the indicator through the AHP method are as follows:

[0184] Step 1: Decompose the multi-objective decision-making problem and establish a hierarchical structure. The hierarchical structure constructed in this embodiment has two layers.

[0185] Step 2: Construct the judgment matrix.

[0186] When analyzing the degree of danger of risk factors, the influence of factors at the same level on road performance is compared pairwise to form a pairwise judgment matrix A = (a ij ) n×n , element a ij The value of refers to the importance scale table. The importance scale table of this embodiment is shown in Table 13.

[0187]

[0188] In the above matrix, a ii =a jj =1,a ij =1 / a ji .

[0189] Table 13 Judgment matrix scale meaning table

[0190] Proportional Scale meaning 1 Indicates that two factors have the same importance when compared 3 Indicates that one factor is slightly more important than the other factor. 5 Indicates that one factor is significantly more important than the other factor when compared with the other factor. 7 Indicates that one factor is more important than the other. 9 Indicates that one factor is extremely more important than the other. reciprocal <![CDATA[Factor i is a when compared with factor j ij , and j is judged as 1a when compared with i ji > 2，4，6，8 are the medians of the above adjacent judgments.

[0191] Step 3: Consistency check.

[0192] The importance weights of the elements relative to their criteria are calculated from a single judgment matrix. After constructing the judgment matrix A, the maximum eigenvalue λ of the judgment matrix A is obtained by using the square root method. max By normalizing its feature vector W, we can get the importance weight of each factor.

[0193] AW=λ max W;

[0194] Among them, A is the judgment matrix; λ max is the maximum eigenvalue of the judgment matrix A; W is the maximum eigenvalue λ of the judgment matrix max The corresponding eigenvector is the weight vector.

[0195] In order to avoid the phenomenon of contradictory importance of factors in the judgment matrix, a consistency test is required. The consistency test formula is as follows:

[0196]

[0197] Among them, n is the order of the judgment matrix A; CR is the random consistency ratio; CI is the consistency index; RI is the average random consistency index. As shown in Table 14.

[0198] Table 14 Random consistency index RI

[0199] Matrix order 1 2 3 4 5 6 7 8 9 RI 0.00 0.00 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0200] According to the experience and judgment of many experts, the above steps are adopted to obtain the first-level indicator judgment matrix table 15, the second-level indicator judgment matrix table 16, table 17, table 18, table 19, table 20, and finally the evaluation indicator weight distribution is shown in Table 21.

[0201] Table 15 First-level indicator judgment matrix

[0202]

[0203] Table 16 Secondary indicator judgment matrix

[0204]

[0205] Table 17 Secondary indicator judgment matrix

[0206]

[0207] Table 18 Secondary indicator judgment matrix

[0208]

[0209] Table 19 Secondary indicator judgment matrix

[0210]

[0211] Table 20 Secondary indicator judgment matrix

[0212]

[0213] Table 21 Rating index weight distribution summary

[0214]

[0215]

[0216] After the weight coefficients of the above 12 indicators are determined by the above AHP method, a questionnaire survey method similar to that in S4 can be conducted to determine the final score to evaluate the road performance of the unpaved road.

[0217] S4. Determine the comprehensive weight evaluation and obtain the total evaluation score, and establish a relationship between the total evaluation score and the evaluation level.

[0218] 1. Construct an expert questionnaire

[0219] This questionnaire survey is mainly for all people majoring in civil engineering. The questionnaire is divided into four parts, mainly including basic information and the scoring of road performance influencing factors. It is sent to the pre-selected expert group through the "Wenjuxing" platform to obtain sample data through anonymous survey, and the data is highly authentic.

[0220] The questionnaire is scored at four levels, 0-2 for very unimportant, 3-4 for moderately important, 5-7 for important, and 8-10 for very important. Score the above 12 indicators in turn. Obtain the identity of each investigator and the years of work in the relevant field. Clean the collected data and exclude invalid questionnaires and outliers. Use statistical software to analyze the data and calculate the average score of each indicator.

[0221] 2. Determination of indicator weights

[0222] Calculate the average score of each indicator to ensure that the score of each indicator reflects the overall trend. The formula is:

[0223]

[0224] Where N is the total number of indicators;

[0225] Standardization adjusts the data to make the comparison between different indicators fairer. Common standardization methods include Z-score standardization, which standardizes the data by subtracting the mean and dividing by the standard deviation;

[0226] After normalization, the weight calculation formula is:

[0227]

[0228] Verify the rationality of the weights, conduct sensitivity analysis and expert review. Sensitivity analysis ensures the robustness of weight allocation by checking the impact of weights on the final results. At the same time, expert review provides professional judgment to ensure that weight allocation is in line with industry standards and professional practices;

[0229] It should be noted that when there is a high correlation between indicators or the scores of some indicators are unevenly distributed. In these cases, more complex statistical methods may be needed to determine the weights to ensure the accuracy and reliability of the results.

[0230] 3. Calculation of comprehensive weight evaluation.

[0231] Through the questionnaire survey, the weights of the 12 indicators were obtained, as shown in Table 22.

[0232] Table 22 Rating index weight distribution summary

[0233]

[0234]

[0235] The corresponding grade scores for each indicator are shown in Table 23.

[0236] Table 23 Quantitative score range of grade classification

[0237] Level classification Points A 90～100 B 80～89 C 70～79 D ≤60

[0238] 1. Climate factor—E1, weight coefficient is q1=0.063

[0239] 2. Humidity—E2, weight coefficient is q2=0.071

[0240] 3. Flatness index IRI—(F1), weight coefficient is q3=0.079

[0241] 4. Gap average value K cp —(F2), weight coefficient is q4=0.074

[0242] 5. The percentage of gaps greater than 5 mm is δ—(F3), and the weight coefficient is q5=0.084

[0243] 6. Crack area ratio C-(D1), weight coefficient is q6 = 0.082

[0244] 7. Maximum wheel track depth H max —(D2), weight coefficient is q7=0.091

[0245] 8. Unconfined compressive strength index q u —(S1), weight coefficient is q8=0.092

[0246] 9. Shear strength index —(S2), weight coefficient is q9=0.079

[0247] 10. CBR—(S3), weight coefficient is q 10 =0.102

[0248] 11. Rebound modulus E r —M1, weight coefficient is q 11 =0.086

[0249] 12. Reaction modulus K-M2, weight coefficient is q 12 =0.098

[0250] The sum of weight coefficients q:

[0251]

[0252] Total evaluation score Q:

[0253]

[0254] In this embodiment, a field airport soil foundation pavement project is taken as an example. The area is located in southeastern China and belongs to the subtropical monsoon climate zone, with four distinct seasons, abundant rainfall and sufficient sunlight. It is hot and rainy all year round, with an average temperature of about 16°C to 23°C throughout the year, and an average annual precipitation of 1576.3 mm. It is known that the soil foundation height of the pavement is 2.0m. Through measurement and calculation, it is known that the flatness index is 0.6cm, the average gap is 0.7cm, and the percentage of gaps greater than 5mm is about 20%. There are relatively many cracks, and the largest area can reach 45%. The maximum wheel track depth is 8cm. The CBR measured by the field test is 10%. After indoor tests, it is measured that the soil foundation belongs to silty clay, and the unconfined compressive strength reaches 1.1MPa at a compaction degree of 95%, c=120.39Kpa, φ=42.82°, the rebound modulus is 63.3MPa, and the response modulus is 49MPa. According to the design parameters of the pavement, the values ​​of the following evaluation indicators are determined, and the performance of the soil base is calculated.

[0255] According to the above AHP analytic hierarchy process and expert questionnaire survey comprehensive weight evaluation standard scores are as follows Table 24:

[0256] Table 24 Scoring

[0257]

[0258]

[0259] The total evaluation score Q formula is used to calculate the score, and then a comprehensive evaluation of the pavement's road performance is conducted.

[0260] The calculation results are: Q = 84.5823 points (expert questionnaire survey method) and Q = 86.2357 points (AHP hierarchical analysis method). According to the two methods, we can get:

[0261] The road performance grade of the soil base is Class B.

[0262] Therefore, the present invention adopts the above-mentioned comprehensive evaluation method for the road performance of unpaved pavements, by establishing an indicator hierarchy model including multiple evaluation indicators and determining the grading standards, determining the weight of each evaluation indicator, and the comprehensive weight evaluation and grade classification method, to provide a comprehensive and accurate evaluation system for the road performance of unpaved pavements.

[0263] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A comprehensive evaluation method for road performance of unpaved pavement, characterized in that: The following steps are involved: S1. Set factors affecting road performance; S2. Establish an indicator hierarchy model and determine the grading standards; S3. Determine the weight of each evaluation index; S4. Determine the comprehensive weight evaluation and obtain the total evaluation score, and establish a relationship between the total evaluation score and the evaluation level.

2. The method for comprehensive evaluation of road performance of an unpaved road surface according to claim 1, characterized in that: The index hierarchy model of step S2 includes five first-level indicators, namely, the road environment and humidity condition index E, the road surface flatness index F, the road surface damage index D, the road surface strength index S and the road surface stiffness index M. Each first-level indicator includes several second-level indicators, and four classification standards are determined according to the second-level indicators.

3. The method for comprehensive evaluation of road performance of unpaved pavement according to claim 2, characterized in that: The environmental and humidity condition index E includes two secondary indicators: climate factor E1 and humidity E2: Climate factor E1 is divided into four levels according to the average maximum temperature of the hottest month, the annual extreme minimum temperature and the annual precipitation; Humidity E2: For the original road surface, it is determined by the average consistency within 80cm below the bottom of the road groove. The calculation formula is: Among them, ω c Indicates the average consistency of the soil; ω L represents the liquid limit of soil; ω p represents the plastic limit of soil; ω represents the average water content of soil; Among them, ω i It indicates the natural water content of the i-th layer of soil within 80cm below the bottom of the trench, with each layer consisting of 10cm; ω ci represents the density of the i-th layer; ω Li Indicates the liquid limit water content of the same layer of soil; ω pi Indicates the limit moisture content of the same layer of soil; It indicates the arithmetic mean consistency of the soil within 80cm below the bottom of the trench.

4. The method for comprehensive evaluation of road performance of unpaved pavement according to claim 2, characterized in that: The road surface roughness index F includes: The calculation formula of the roughness index IRI is: Among them, Δ yi Indicates the elevation difference between adjacent measuring points; Δ xi Indicates the distance between adjacent measurement points; L indicates the total length of the measurement; z indicates the total number of measurement points; Gap average value K cp , refers to the average distance between the road surface and an ideal plane, and the calculation formula is: Among them, R i represents the gap value; a represents the number of gaps; The percentage of gaps greater than 5 mm, δ, refers to the percentage of measurement points with gaps greater than 5 mm in the total number of points. The calculation formula is: in, Indicates the number of measuring points with a gap greater than 5 mm.

5. The method for comprehensive evaluation of road performance of unpaved road surface according to claim 2, characterized in that: The road surface damage index D includes: The crack area ratio C is the ratio of the converted pavement crack area to the total area of ​​the tested road section; Maximum wheel track depth H max , the calculation process is as follows: Among them, σ min Indicates the minimum bearing capacity factor; P K Indicates the mass of a single wheel; B indicates the width of the rigid wheel; D indicates the wheel diameter; represents relative thrust; α represents conversion factor; The maximum rut depth is limited to: H max <0.07D; The minimum load-bearing coefficient is the minimum value of the tire pressure that the pavement structure can withstand without damage, which is:

6. The method for comprehensive evaluation of road performance of unpaved road surface according to claim 2, characterized in that: Pavement strength index S includes: Unconfined compressive strength index q u , unconfined compressive strength refers to the ultimate strength of the specimen to resist axial pressure without lateral pressure; Shear strength index c, Refers to the index of soil foundation strength and resistance to deformation; California Bearing Ratio CBR, through DCP test, establishes the relationship between DCP test penetration rate PR and CBR as follows:

7. The method for comprehensive evaluation of road performance of unpaved road surface according to claim 2, characterized in that: The road surface stiffness index M includes: Rebound modulus E r , refers to the ratio of the deformation of the soil under load to the deformation after the load is removed, as follows: Among them, σ v represents vertical stress; ε v represents the vertical strain; The reaction modulus K is the proportional constant between pressure and deflection value, as follows: Where P represents unit pressure and l represents deflection value.

8. The method for comprehensive evaluation of road performance of unpaved road surfaces according to claim 1, characterized in that: Step S3 includes determining the weight coefficient of the indicator by the AHP method, the steps are as follows: S31. Decompose multi-objective decision-making problems and establish a hierarchical structure; S32, constructing a judgment matrix A; S33, find the maximum eigenvalue λ of the judgment matrix A max , normalize its eigenvector W and obtain the weight of each factor: AW=λ max W; S34, perform consistency check, the formula is as follows: Where n represents the order of the judgment matrix A; CR represents the random consistency ratio; CI represents the consistency index; RI represents the average random consistency index; Through the above steps, the first-level indicator judgment matrix, the second-level indicator judgment matrix and the evaluation indicator weight distribution table are obtained.

9. The method for comprehensive evaluation of road performance of unpaved road surface according to claim 1, characterized in that: Step S4 specifically includes: S41. Construct an expert questionnaire, including basic information and the scoring of road performance influencing factors, and the questionnaire scores are divided into four levels; S42. Determine the indicator weights, including: S421. Calculate the average score of each indicator using the following formula: Where N is the total number of indicators; S422, standardization processing; S423, normalization processing, the weight calculation formula is: S424, verify the rationality of weights; S43. Calculate the comprehensive weight evaluation and establish a relationship with the four grading standards.

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