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Wanyan et al.

AN EXPERT SYSTEM FOR DESIGN OF LOW-VOLUME ROADS OVER EXPANSIVE SOILS by Yaqi Wanyan, PhD, EIT The University of Texas at El Paso 6815 Ripplemoor Ct. Sugar Land, TX 77479-2168 Phone: 832-605-6265 (H), 915-355-4649 (C) Email: [email protected] Imad Abdallah, MSCE, EIT The University of Texas at El Paso Department of Civil Engineering, M-105B 500 W. University Ave El Paso, TX 79968 Phone: 915-747-8907 Email: [email protected] Soheil Nazarian, PhD, PE The University of Texas at El Paso Department of Civil Engineering Annex Rm 207 500 W. University Ave El Paso, TX 79968 Phone: 915-747-6911 Email: [email protected] Anand J. Puppala, PhD, PE The University of Texas at Arlington UTA Box 19308 Arlington, TX 76019-0308 Phone: 817-272-5821 Email: [email protected] Date: October 27, 2009 Word Count: 4741 words + 3 tables + 7 figures = 7241

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.

Wanyan et al.

AN EXPERT SYSTEM FOR DESIGN OF LOW-VOLUME ROADS OVER 1 EXPANSIVE SOILS 2

ABSTRACT 3 This paper summarizes the development of an expert system to assist pavement engineers in 4 designing more realistic and practical low-volume roads in clayey areas. This expert system 5 combines numerical and engineering analyses with heuristic information about the site to 6 recommend optimal design, remediation and construction alternatives. Common distress 7 types are considered. In particular, numerical analysis is incorporated to predict the potential 8 for longitudinal cracking of the section, which was reported by a survey throughout Texas to 9 be the most prevailing distress. After assessing the structural capacity, remediation methods 10 are proposed to address the problem of pre-mature failure of low-volume roads on high-PI 11 clays. Cost-benefit analyses are added to compare cost-effectiveness of recommended 12 alternative strategies. Finally a case study is presented in detail to illustrate the use of the 13 software. Based on preliminary study of typical low-volume roads in Texas built over 14 expansive subsoils, thicker and stronger pavement layers do not guarantee better performance. 15 Instead, addressing the environmental factors on changes in subgrade properties play a bigger 16 role in serviceability and performance. 17

1 INTRODUCTION 18 Rural, low-volume roads are significant parts of any transportation system. Roads that are 19 constructed on soft and problematic soils are the source of frequent maintenance problems. 20 These problems are further exaggerated on expansive clayey subgrade. Current design 21 practices often yield thick pavement structures to minimize the impact of expansive 22 subgrades. In many cases, these pavements still fail prematurely. The problematic nature of 23 high PI clays, despite the fact that volumetric changes such as heaving are sometimes 24 considered in the design, is of concern since they contribute to the roughness and longitudinal 25 shrinkage cracking of the road, and as such the loss of their functional serviceability. It is 26 imperative to improve the design and laboratory procedures to address expansive subsoil 27 conditions and then design pavements accordingly to extend the life expectancy of these 28 roads. These considerations, often heuristic in nature, are incorporated in an expert system 29 program. The expert system approach is a knowledge-based problem solving method which 30 can be used to solve highly domain specific problems in a similar manner as human reasoning 31 process, and with similar results, as that of human experts. 32

The intent of the Expert System for Pavement Remediation Strategies (ExSPRS) 33 software is to cultivate the vital features of strategies for improving performance of low-34 volume flexible roads built over expansive clayey subgrades. The flow chart of ExSPRS is 35 shown in Figure 1. The main purpose of the software include: (i) identifying the most 36 prevailing distresses by using performance and structural evaluation models to address the 37 problems of premature failure; (ii) qualifying and quantifying current remediation procedures 38 used to mitigate such distresses; and (iii) providing cost-benefit analyses for design and 39 construction of alternatives. 40

2 DESIGN CONSIDERATIONS 41 A realistic low-volume road design relies on many factors where the problematic 42 characteristics of expansive subgrade soils play a major role. Clayey subgrades exhibit 43 exceptionally low strength and tend to swell when they become wet; and they are highly 44 brittle and shrink when they become dry. This variation in properties results in different types 45 of damage through seasonal wet and dry cycles. Besides the strength and stiffness changes 46 throughout the year, excessive volume changes caused by swelling or shrinkage of expansive 47 soils can exert enough pressure to damage the pavements and cause maintenance problems. 48


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FIGURE 1: Conceptual Flowchart of ExSPRS Software 32

In order to identify major design considerations, a questionnaire was sent to all 25 33 Texas Department of Transportation (TxDOT) districts. Eighteen districts reported having to 34 deal with high PI clayey subgrades. Longitudinal cracking was reported as the most frequent 35 distress experienced on their low-volume roads, followed by fatigue cracking, subgrade 36 rutting, subgrade shear failure and excessive roughness. These prevailing distresses can be 37 categorized into two groups: (i) severe longitudinal shrinkage cracking, excessive roughness 38 and swelling due to substantial volume change; and (ii) fatigue cracking, rutting and subgrade 39 shear failure due to inadequate support. The ExSPRS program provides four evaluation 40 models to determine whether an existing or a selected pavement structure meets most relevant 41 structural and performance criteria specially targeted for low-volume roads over high PI clays. 42

2.1 Longitudinal Cracking Model 43 Current TxDOT design process does not have a formal design procedure for mitigating 44 longitudinal cracking caused by underlying expansive soils. One of the main focuses of the 45 ExSPRS was to develop and incorporate a module for this purpose. Extensive laboratory 46 investigation and numerical modeling were carried out to address this issue. Shrinkage 47 induced longitudinal cracks are believed to be initiated in the subgrade (1). The upward climb 48 of cracks in the pavement structure is due to the bond between the subgrade and the base and 49 to the low tensile strength of the base course. If the tensile strength of the asphalt layer is also 50 inadequate, the crack may propagate to the surface (2). Loss of moisture in clayey subgrades 51


Wanyan et al.

would cause tensile stresses (σss) as a function of its elastic modulus (E) and tensile shrinkage 1 strain due to drying (εss). If σss exceeds the tensile strength (σt) of the subgrade material, a 2 fracture will develop according to: 3

Essss ⋅= εσ (1) 4

However, both εss and E change significantly with moisture content variation. With 5 further drying of the material after the crack initiates in the subgrade, , numerical analysis is 6 needed to examine whether the initial shrinkage crack is stable or whether it will propagate 7 through the base and asphalt layers. The following two major challenges had to be overcome 8 in implementing a finite element model for this purpose: 9

10 (i) Estimatation of the variations in shrinkage strain (εss), tensile strength (σt) and modulus 11

(E) as a function of moisture content (MC). Even though a number of models that utilize 12 suction (instead of moisture content) to estimate these properties are available, no 13 moisture-content based models could be found. The contention of the practcing engineers 14 associated with this project was that it would be more practical to implement moisture 15 content-based models for their convenience.. This challenge required extensive laboratory 16 testing and data analysis as discussed below. 17

(ii) Estimation of the initiation and propagation of the cracks in a fashion that can be utilized 18 automatically by pavement engineers that are not experts in finite element modeling. This 19 task was achieved through an algorithm that is also discussed below. 20

21 Five representative high PI clayey sites in Texas were carefully selected to develop 22

generalized mathematical relationships for εss, σt and E as a function of MC. Volumetric 23 shrinkage strain tests were conducted on all soils and extensive statistical and trend analyses 24 were performed as discussed in (3). The shrinkage strain (εss) of individual soils could be 25 estimated from the following relationship with R2 values better than 0.9 (3): 26

( )[ ]22* 1 NMCAss −=ε (2) 27

where A* is a curve-fitting parameter and NMC is the normalized moisture content 28 defined as the actual moisture content divided by the optimum moisture content for a given 29 soil. As shown in Figure 2, parameter A* is well-correlated to some of the readily-available 30 index properties of the soils tested. Based on R2 values (shown in Figure 2), A* is reasonably 31 well-correlated to plasticity index (PI), liquid limit (LL), and optimum moisture content 32 (OMC) and marginally correlated to the maximum dry density (MDD). The following 33 equation is proposed (3) to estimate A*: 34

MDDOMCLLPI

MDDMDDOMCOMCLLLLPIPI

WWWWWAWAWAWA

A+++

×+×+×+×=

***** (3) 35

where Ai* is parameter A* obtained from index property i, Wi is the weighting factor 36

for index parameter i. The recommended Wi are 4, 2 and 1 for R2 values greater than or equal 37 to 0.8, between 0.6 and 0.8, and less than or equal to 0.6, respectively. 38

Indirect tensile strength (IDT) tests were performed on two specimens of each clay 39 sample at six different moisture contents from optimum to dry. The variations in the average 40 IDT strengths and normalized moisture contents for all soils followed a unique trend with 41 approximately 25 psi peak strength under dry condition that decreased smoothly to 42

43 44


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FIGURE 2: Correlations between Parameter A* and Index Properties of Soils 1

approximately 5 psi at OMC. The curve-fitted mathematical relationship to estimate 2 expansive soil tensile strength (σt) from NMC is in the form of (3): 3

NMCt e ×−= 779.27.35σ (R2=0.89) (4) 4

Lastly, as subgrade stiffness increases with the loss of moisture, a relationship between 5 subgrade modulus (E) and MC were also obtained using same approach as for shrinkage strain 6 (εss). A relationship in the following form was selected to predict modulus: 7

( )2**n (-C)BexpE NMC×+= (5) 8

where En is the normalized modulus (modulus at a given moisture content divided by 9 modulus at optimum moisture content), B* and C* are curve-fitted parameters that can be 10 estimated using index properties similar to A* (3). 11

These empirical relationships were used in a finite element (FE) modeling algorithm as 12 shown in Figure 3. NMC is used as a controlled input to calculate εss using Equation 2. The 13 same NMC value is also used to predict σt by Equation 4 and En by Equation 5. NMC is 14 reduced until the cracking is initiated (i.e. σss ≥ σt). . 15

Two FE models were developed. A two-dimensional plane-strain linear elastic model 16 was developed to estimate the moisture content threshold, MCI, which controls the onset of 17 the initial longitudinal shrinkage cracking in the subgrade. This stand-alone model, which is 18 linked to the main expert system, does not require any other software licenses to execute. 19 More importantly, the mesh generation and optimization are done automatically, so pavement 20 engineers are not required to spend time on FE modeling details but rather can focus on the 21 analysis of the results. 22

23 24


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FIGURE 3: Flow Chart for Estimating Longitudinal Cracking 1

Another nonlinear plastic-elastic fracture model was also developed using a 2 commercial FE modeling package to further study crack propagation within the pavement 3 layers. The moisture content at which the cracks will appear at the surface, MCP, is estimated 4 utilizing a Mode I fracture model (4). The main constraints in using the fracture model in the 5 expert system were the license requirements and the need for user interaction in FE model 6 setup. To avoid these items, extensive parametric studies on three- and four-layer pavements 7 were carried out with both FE models (see Ref. 4). The moisture level to initiate cracking 8 from the two FE models agreed well with longitudinal cracks observed either at pavement-9 shoulder interface or within the pavement width where the maximum tensile stress occurs in 10 the subgrade. The cracks shifted away from the shoulder edge when the thickness of the 11 pavement structure above subgrade increased, but the maximum shrinkage stress in the 12 subgrade was not very sensitive to the pavement structure. The moisture content at which a 13 shrinkage crack emerges at the pavement surface, MCP, can be estimated from the following 14 generalized relationship: 15

2.0−= IP NMCNMC (6) 16

where NMCP and NMCI are MCP and MCI, divided by the optimum moisture content 17 of the subgrade, respectively. The required inputs are basic soil index properties such as PI, 18 LL, OMC, MDD and resilient modulus at optimum moisture content. The ExSPRS program is 19 fully automated and does the FE in background. Except the resilient modulus, all other 20 parameters are readily available to the designers. 21

2.2 Roughness Model 22 To select the most feasible volumetric change prediction model, laboratory test results and 23 field measurements were compared for seven empirical relationships (4). Based on results, the 24 potential vertical rise (PVR) method was selected to estimate the differential movements. As 25

tσ

ssε

tss σσ <


Wanyan et al.

per TxDOT specifications, soil properties including PI, LL, MC and percentage of soil 1 particles passing sieve #40 (P40) are used to estimate the PVR value at both the top and 2 bottom of each layer and the total PVR value. To accurately automate PVR estimates in this 3 study, the charts provided in TxDOT specifications were digitized and curve-fitted to develop 4 the following equation to calculate PVR based on volumetric swell: 5

221 PCPCPVR ⋅+⋅= (7) 6

where P is the average load of the analyzed layer (psi), C1 and C2 are curve-fitted 7 constants that are a function of percentage volumetric swell (α) as reported in Wanyan, et al. 8 (4). 9

International roughness index (IRI) was used to define characteristic of the 10 longitudinal profile. The IRI model proposed by Lytton, et al. (5) was used. The model has 11 the following format: 12

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎠⎞

⎜⎝⎛−−+=

i

tIRIIRIIRI i

βρexp)2.4( 00 (8) 13

where IRI0 is the initial IRI; ρi and βi are roughness parameters based on both traffic 14 and total vertical movements (ΔH). In roughness model, the PVR value predicted using 15 Equation 7 is used as total vertical movement (ΔH) in Equation 8. This value only takes into 16 consideration the maximum possible heaving. 17

2.3 Structural Models 18 Two structural checks are recommended for low-volume roads: (i) fatigue cracking and 19 rutting; and (ii) subgrade shear failure. The allowable number of load repetitions (remaining 20 life in ESALs) to cause fatigue cracking (Nf) and rutting (Nd) were calculated using predicted 21 horizontal tensile strain (εt) and vertical compressive strain (εc) at critical locations in the 22 following equations: 23

32 )()( 11ff

tf EfN −−= ε (9) 24

5)(4f

cd fN −= ε (10) 25

where constants f1 through f5 are empirically-derived parameters. In this model, values 26 of 0.0796, 3.291, 0.854, 1.365 x 10-9, and 4.477 are used for f1 through f5, respectively. The 27 linear elastic multi-layer program WES5 (6) was adopted and modified to conduct this check. 28 The required input for this subroutine include: total number of layers, modulus, thickness and 29 Poisson’s ratio for each layer. 30

On low-volume roads, with reasonably thin pavement structures, subgrade shear 31 failure due to heavy truck wheel loads is possible, particularly for wet clayey subgrades (7). 32 To ensure that the pavement provides adequate cover to protect the subgrade against 33 overstressing, TxDOT utilizes the Texas triaxial design method to establish the depth of cover 34 required (Dcover) to avoid shear failure. Fernando, et al. (7) also recommended the modified 35 triaxial method (MTRX) to check required base thickness (Dbase). The required inputs are: 36 design wheel load and Texas triaxial test results (designated as TEX-117-E). The output 37 shows pass/fail flag and Dcover, Dbase numerically for user’s reference. 38


Wanyan et al.

3 PREVENTATIVE AND REMEDIAL STRATEGIES 1 Many preventative and remedial strategies can be used to improve the detrimental properties 2 of expansive soils. Six modification strategies were selected to improve subgrade strength 3 and stiffness (which include stabilization, geosynthetic reinforcement, undercut and backfill); 4 and to minimize moisture variation (which include moisture control, deep dynamic 5 compaction and decreasing clay content). Appropriate methods from either or both categories 6 are recommended based on the evaluation results discussed previously. 7

3.1 Stabilization 8 Clayey soils are often stabilized with calcium based stabilizers to improve their engineering 9 properties including strength, volumetric change potential and permeability. This method is 10 recommended for all failure types discussed above. TxDOT standardized guidelines for 11 modification and stabilization of soils and bases provided the main decision making guidance 12 for this process. For regular subgrade stabilization, PI and percent fines are used as the 13 primary input to select appropriate stabilizers. ExSPRS program also provides strategies for 14 dealing with sulphate and organic rich soils. 15

3.2 Use of Geosynthetics 16 Geosynthetics have proven to be versatile ground modification materials. Geosynthetic 17 reinforcement is targeted for subgrade improvement by placing it at subgrade-base interface. 18 The FHWA design method by Holtz et al. (8) is implemented to obtain the minimum required 19 cover depth based on digitalized design charts. This step ensures the insertion of geosynthetic 20 at recommended location (near top of subgrade) will be fully functional without losing its 21 anchorage strength. 22

3.3 Moisture Control Methods 23 This remediation strategy focuses on three main categories of using moisture barriers; 24 improving drainage; and treating nearby vegetations. Vertical moisture barriers (9) isolate the 25 subsoils from the climatic changes and thus minimize moisture variations. In drier season 26 when most of the new pavement is constructed, the role of the vertical moisture barrier is to 27 prevent subgrade access to free water. On the other hand, under wet conditions, the barrier 28 will prevent excessive drying of the subgrade soil, especially under pavement shoulders, and 29 thus to prevent longitudinal shrinkage cracking from happening. 30

Lack of adequate surface drainage is one of the critical factors leading to problems 31 with expansive subgrade soils. Use of specially designed drainage systems will significantly 32 reduce the time moisture is retained in the pavement system. They also help in minimizing 33 moisture change in the subgrade and make it more stable. Keller and Sherar (10) provided 34 detailed drainage designs specially targeted to low volume roads, including the use of out-35 sloped, in-sloped and crown sections, placement of culverts, rolling dip cross-drains 36 frequently, and using water bars, inlets and outlets control methods. 37

Plant transpiration (water extraction by roots) is generally more significant than 38 subgrade soil evaporation due to the low permeability of pavement surface, especially for 39 newly constructed roads. Vertical root barrier is one treatment that has been found to redirect 40 root growth to lower levels of the soil, thus reducing damage (11). Big tree removal provides 41 another choice for vegetation treatment. A rule of thumb to avoid soil shrinkage and damage 42 to pavement is to keep the distance to height of tree ratio (D:H) greater than 1.0-1.5 and 43 remove big trees within that range (12). 44

3.4 Other Methods 45 Poor subgrade soil can simply be removed and replaced with high quality fill. This method is 46 a simple procedure that does not require any specialized equipment. However, unless a 47 suitable backfill material is available near the job site, removal and replacement is generally 48


Wanyan et al.

much more expensive than the use of additives. For lower classification roads, economic 1 constrains have to be taken into account. 2

When undercut and backfill is not economically feasible, “decreasing clay content” 3 provides an alternative. As the name implies, this process is to dilute expansive soils with 4 non-expansive fill. It is less time consuming and cheaper compared to undercut and backfill 5 when quantities of non-expansive fills are limited. 6

4 COST AND BENEFIT ANALYSIS 7 To aggregate and compare recommended alternatives, cost-benefit analysis is performed. For 8 low volume roads, which typically have low daily traffic, the user costs can be considered 9 minimal and omitted. For the same reason, construction activity timing of low-volume roads 10 is not critical. The agency costs were calculated by using the unit price information obtained 11 from the RS Means CostWorks Data for Heavy Construction. Cost analysis component 12 assembles initial construction costs for each scenario (including original design and all 13 alternatives), while Benefit analysis component carries out “before-and-after” performance 14 analysis. With the selection of each remediation strategy, the evaluation module is executed 15 again to compare performance improvement with the newly recommended approach(es). 16

Original design and each recommended remedial strategy are compared side by side 17 for their cost and benefit estimations in a tabular format. The cost and benefit analysis results 18 act as an open door to realistic alternatives. The user is encouraged to further explore different 19 possibilities and compare multiple preference criteria in order to reach a better informed 20 decision. 21

5 CASE STUDY 22 In this study, five representative high PI sites in Texas (Fort Worth, Houston, San Antonio, 23 Paris and Atlanta) were selected to verify the outcomes of ExSPRS. Fort Worth case will be 24 illustrated in detail. Field data collections, which include instrumentation systems, site 25 information and field measurements, are briefly described. Required inputs are compiled and 26 presented in tabulate format. Possible failures/problems are predicted and feasible remedial 27 strategies are recommended. Finally, cost-benefit analysis is discussed to help the user reach 28 effective solutions. 29

Severe longitudinal cracking and rutting were observed at the Fort Worth site. The side 30 slope is covered with grass on both sides of the road. The area next to the pavement shoulder 31 is irrigation farmland. No drainage ditch is available on either side of the pavement. After 32 nondestructive testing with FWD and DCP, soil samples were carefully retrieved from this 33 site. Laboratory tests performed include Atterberg limits, Texas triaxial, resilient modulus, 34 permanent deformation, unconfined compression strength (UCS), indirect tensile strength 35 (IDT) and volumetric shrinkage strain tests. Field moisture and matric suction sensors were 36 carefully embedded close to one another to ensure that the data from both systems represent 37 the same soil conditions. Figure 4 presents variations in soil moisture contents, monthly 38 average soil moisture contents, rainfall amounts, and pavement elevation changes with time at 39 this site. This data is correlated with the new and reappearance of old pavement cracks at the 40 site. Laboratory tests data and field measurements are summarized and compiled in Table 1 as 41 input for ExSPRS. 42

The evaluation results from ExSPRS are shown in Figure 5. For estimated 43 accumulated design ESALs of 1 million, the original design passed the fatigue cracking and 44 substantially failed the rutting criteria. The Texas triaxial check for subgrade failure proposed 45 a minimum required pavement structure of 15 in. (as opposed to 10 in. observed at the site). 46 The modified triaxial method (MTRX) proposed a minimum base thickness of 12 in. to ensure 47 that the subgrade is not overstressed. A total potential vertical rise (PVR) of 2.6 in. was more 48 than the 2 in. limit specified for secondary roads. The estimated IRI of 1.58 m/km was 49


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FIGURE 4: Summary of Field Data Measurements for Fort Worth Site 1

considered acceptable for farm to market road. The longitudinal shrinkage cracking model 2 suggested that the subgrade would start to develop longitudinal shrinkage cracks when the 3 moisture content dropped below 21.6%, and that the crack would appear on pavement surface 4 when the moisture content dropped below 16.8%. The ExSPRS program also presents a 5 plot of the top 50 largest tensile stress points within subgrade simulated by the linear elastic 6 FE model, as shown in Figure 6. The most likely location of the crack is near 1/3 lane width. 7 (2 to 7 ft) from the edge, where most of the top 50 largest tensile stress points are located. 8 Figure 7 shows pictures of the Fort Worth site conditions at completion of this study. The 9 longitudinal cracking was developed near outer-wheel lane towards pavement edge. The 10 distressed area was about 3 ft wide. From field observation (see Figure 4) shrinkage crack 11 near outer-wheel became visible when subgrade moisture dropped below 15%. These field 12 observations correspond well with our model estimation. Although the program cannot predict 13 exactly how long it might take for the subgrade desiccation cracks to propagate to the surface, 14


Wanyan et al.

TABLE 1: Fort Worth Case Study Input Data (FM 157) 1 Number of layers 3 Description of layers HMAC Flexible Base Subgrade Thickness (in.) 2 8 200 L

ayer

Pr

oper

ties

Modulus (ksi) 350 55 12 Design ESALs (millions) 1 Analysis period (years) 10 Initial serviceability index 4.0 Reliability (in decimal) 0.8 Design wheel load (kips) 18 Tire Pressure (psi) 100 Road length (mile) 1 Total number of lanes 2 Lane width (ft) 12 Depth of treated subgrade (in) 12 Percent of time pavement is exposed to saturation moisture level (%) 1 to 5

Des

ign

Prop

ertie

s

Pavement drainage quality Good Subgrade Modulus during wet season (ksi) 7 PI (%) 29 LL (%) 61 P200 (%) 85 OMC (%) 24 Dry MC (%) 15.1 MDD (pcf) 91.5 Angle of internal friction (deg) 35 Cohesion of soil (psi) 3.6 PVR limit (in) 2

Soil

Prop

ertie

s

Sulfate content (ppm) 358

FIGURE 5: Fort Worth Case Study Evaluation Considerations Outcome Screen 2


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it is safe to assume that after just one typical drying cycle (typically a few weeks according to 1 local rainfall history data) this bottom-up shrinkage cracking will start causing problems. 2

FIGURE 6: Fort Worth Case Study Plot of Top 50 Largest Tensile Stress Points in 24 Subgrade 25

FIGURE 7: Fort Worth Case Study Photos of Site Current Conditions 26

Several remedial strategies were recommended and selected for illustration purposes. 27 The program recommends the use of calcium-based stabilizers with lime to a depth of 12 in. 28 Alternatively, a geosynthetic layer directly laid on top of the subgrade would have provided 29 adequate performance. Moisture control in terms of improving pavement drainage and 30 applying vegetation treatment would have also prolonged the life of the pavement. An 31 undercut and backfill of 18 in. would have reduced the pavement distress to acceptable levels. 32

Table 2 represents the cost and benefit analysis results (color coded in tabulated format 33 for demonstration purpose). The “Parameters Affected in Evaluation Module” section shows 34 changed parameters for the selected remediation strategies. Red bold numbers are changed 35 parameters, assumed based on literature reviews and recent research data. The “Cost 36 Analysis” section shows cost analysis results. The original design costs about $330,000/mile. 37


Wanyan et al.

TABLE 2: Fort Worth Case Study Cost and Benefit Analysis Parameters and Results Summary (Regenerated) Remediation Strategies

Original Stabilization Geosynthetics Moisture

Control Undercut &

Backfill Deep Dynamic

Compaction Decrease Clay

Content

Color Code

Parameters

(1) (2) (3) (4) (5) (6) (7) (8) Parameters Affected in Evaluation Module

rM at optimum (ksi) 12 30 50 12 25 18 22 rM at wet (ksi) 7 15 50 14 15 9 10

PI 29 20 29 29 15 29 20 LL 61 45 61 61 40 61 50 OMC (%) 24 26 24 24 21 26 22 MDD (pcf) 91.5 100 91.5 91.5 110 100 100 IDT (psi) 2.9 18 100 2.9 20 5 12 Soil Classification 4 3.8 3 4 3.5 3.9 3.8

Cost Analysis Cost (Thousand $) 330 159 33 49 11 5 4

Benefit Analysis fN (million ESALs) 3.1 2.86 2.64 2.88 2.86 3.01 2.97 dN (million ESALs) 0.04 0.22 8.47 0.18 0.22 0.06 0.08

erDcov (in.) 15 12 4 15 9 14 12 baseD (in.) 12 13 Pass 12 Pass 12 12

PVR (in.) 2.6 0.94 2.6 2.6 0.48 2.6 0.94 IRI (m/km) 1.58 1.38 1.58 1.58 1.35 1.58 1.38

IMC (%) 21.6 23.4 21.6 21.6 18.9 23.4 19.8 PMC (%) 16.8 18.2 16.8 16.8 14.7 18.2 15.4

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Each remediation method costs an extra amount as shown in the table. To justify the benefit 1 of each remedial method, before-after analysis was carried out and results are shown in the 2 “Benefit Analysis” section. The top and bottom parts of Table 3 have been color-coded based 3 on the top parameters used for the corresponding bottom results, except for IRI. The direct 4 input for IRI is the estimated PVR value. 5

Among the recommended remediation strategies, stabilization and moisture control 6 methods are the most expensive ones. However, the soundness of a pavement engineer’s final 7 selection is also dependent on benefit analysis. Different remediation strategies may improve 8 different aspects of pavement performance. For example, stabilization (comparing Table 2 9 col.2 to col.1) increases the allowable fatigue and rutting life, decreases the subgrade shear 10 failure possibility, and also decreases pavement roughness. However, the stabilized subgrade 11 may initiate shrinkage induced cracking at MC of 23.4% as compared to 21.6% for the 12 original design. 13

Table 3 summarizes case study results for all five sites. Evaluation results show 14 different possible distress problems for each site. Results for longitudinal cracking, which was 15 identified as the most prevailing distress problem for lower classification roads, correspond 16 very well with our field measurements. The measured and estimated moisture contents when 17 the longitudinal cracks daylighted are close for Fort Worth, San Antonio and Paris sites. 18 Houston site should experience longitudinal cracking damage when MC drops below 14.1%. 19 No longitudinal cracks were observed at this site because even during the dry season, the 20 average moisture content was above 21%, which was above the predicted crack initiation 21 threshold. The Atlanta site showed longitudinal cracks close to shoulder during the dry 22 season. Due to equipment vandalism, field data for this site at the time of cracking was not 23 available. However based on historical performance, this site is highly susceptible to 24 moisture variation, and thus is expected to experience substantial longitudinal cracking 25 damage. In terms of construction cost, San Antonio case is the cheapest, and Atlanta is the 26 most expensive. Recommended strategies were selected based on evaultion results. San 27 Antonio site was identified by ExSPRS as “inadequate support” and the program 28 recommended strategies to improve structure support, rather than to minimize moisture 29 variation. Based on these case study results, the most critical decision for pavement engineers 30 is to put the limited budget into best use and to select the most cost-effective alternative. For 31 low volume roads built over highly expansive subgrade, thicker and better top layers do not 32 guarantee better performance, as shown in Paris and Atlanta cases. 33

6 CONCLUSTIONS AND RECOMMENDATIONS 34 The following conclusions are drawn from this study: 35 • The behavior of high PI clays change dramatically with moisture content variation. Based 36

on extensive laboratory tests, strength and stiffness changed up to 40 times and volume 37 changed up to 29% from dry to saturated conditions. 38

• The evaluation considerations and remedial strategies discussed herein are targeted to low-39 volume roads in expansive soil areas. More considerations should be given to mitigating 40 the detrimental subgrade properties, improving subgrade strength and stiffness, and 41 reducing subgrade moisture susceptibility. On the other hand, increased layer thicknesses 42 do not seem to guarantee better performance, especially for high PI clays. 43

• Before and after remediation analyses were presented to quantify the effectiveness in 44 terms of evaluation results improvement. No ranked conclusions were provided in output, 45 but rather comparisons of pros (benefit) and cons (cost) were tabulated for the user. It is 46 recommended that the user to carefully compare different design schemes and remediation 47 strategies and select a more reasonable and cost-effective design based on her/his specific 48 needs. 49


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TABLE 3: Summary of Five Baseline Sites Case Study Results

Fort Worth San Antonio Paris Houston Atlanta

Fatigue Cracking Pass Pass Pass Pass Pass Rutting Fail Fail Pass Fail Pass Subgrade Shear Failure (Texas Triaxial) Fail Fail Fail Fail Fail Structural Models

Subgrade Shear Failure (MTRX) Fail Fail Pass Pass Pass PVR Fail Pass Fail Fail Fail

Roughness Models IRI Pass Pass Pass Pass Pass MCI (%) 21.6 19.5 20.7 18.1 25.7

Estimated 16.8 15.1 16.1 14.1 20.0 MCP (%) Observed 16.0 15.0 16.0 --** --***

Eva

luat

ion

Longitudinal Cracking Models

Most Likely Point for Longitudinal Cracking*, ft 3 0 0 7 0

Construction Cost Estimation $ 330k $ 237k $ 562k $ 450k $ 592k Stabilization Yes Yes Yes Yes Yes Geosynthetics Yes Yes Yes Yes Yes Moisture Control Yes No Yes Yes Yes Undercut & Backfill 18 18 18 12 18 Deep Dynamic Compaction Yes No Yes Yes Yes

Recommended Remediation Strategies

Decreasing Clay Content Yes No Yes Yes Yes * Distance from pavement-shoulder interface ** Section has not cracked yet *** Data is not available due to equipment vandalism

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REFERENCES 1 1. Uzan J., Livneh, M. and Shklarsky, E. Cracking Mechanism of Flexible Pavements 2

Transportation Engineering Journal, Proceedings of the American Society of Civil 3 Engineers, February, 1972. pp. 17-36. 4

2. Bell, D.O. and Wright, S.G. Numerical Modeling of the Response of Cylindrical 5 Specimens of Clay to Drying. Publication FHWA/TX-92-1195. FHWA, U.S. Department 6 of Transportation, 1991 7

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