Artificial Intelligence in Engineering 8 (1993) 47-56

Advisory expert system for flexible pavement design

A . T . C . G o b

School of Civil Engineering and Building, Swinburne University of Technology, Melbourne, Victoria 3122, Australia

(Received l May 1992; revised version received 7 July 1992; accepted 13 October 1992)

The adoption of an expert system approach to flexible pavement design could be especially useful, because of the complexity of the design algorithm involving numerous possible combinations of pavement material types and traffic data, and the heavy reliance on empirical correlations, which can vary from one region or state to another. This paper describes the development of an advisory expert system for flexible pavement design that attempts to mimic the design process of pavement engineering specialists. The highly interactive microcomputer expert system was developed, using an expert system shell.

Key words: expert systems, flexible pavement design, knowledge based systems, transportation engineering.

1 INTRODUCTION

There are currently a number of different methods for designing flexible road pavements. Some of these methods are mechanistic in nature, based on stress analysis, while others are empirical or experience based in which the design method is based on past successful practice. The more common methods currently in use include the Asphalt Institute ~ method, the American Association of State Highway and Transportation Officials 2 (AASHTO) method and the National Association of Australian State Road Authorities 3 (NAASRA) method.

The tasks involved in the design of flexible road pavements using any of these methods are often very complex, with a large number of possible algorithms because of the numerous types of pavement materials and traffic data available. At times qualitative and empirical knowledge, known only to experts, is also required. The nature of this problem suggest that the encapsulation of this knowledge using expert system technology could be beneficial. Ritchie 4 has pointed out that, since pavement engineering specialists are typically only to be found within federal and state agencies, universities and private firms, the availability of a pavement design expert system will be of particular benefit to local highway agencies lacking access to specialised human expertise.

Artificial Intelligence in Engineering 0954-1810/93/506.00 © 1993 Elsevier Science Publishers Ltd.

In this paper the prototype expert system PAVEDKB (Pavement Design Knowlede Base) for flexible pavement design is described. The expert system is based on the domain knowledge from the NAASRA 3 design method.

Some recent examples of expert systems in transporta- tion engineering include pavement overlay design, 4 pavement rehabilitation, 5 speed zone determination, 6 traffic signal installation, 7 asphalt pavement construc- tion s and the design of highway safety structures. 9'~°

47

2 NAASRA FLEXIBLE PAVEMENT DESIGN M E T H O D

Figure 1 shows the typical structure of a flexible pavement. Generally, the highest quality materials are at or near the surface. The wearing surface usually consists of a bituminous seal or asphalt, although for low-cost roads this may comprise untreated or surface- treated earth. The base or subbase may comprise more than one layer of material. The material used in the base must be of high quality in order to distribute the stresses created by the wheel loads acting on the wearing surface. This would minimise the stresses transmitted to the subgrade and ensure minimal deformation of the subgrade. As locally available materials are widely used for the base course, the materials preferred vary widely in different countries as well as different locations of a country. It may also be necessary to treat some materials with lime or cement in order to improve their engineering

48 A.T.C. Goh

&_.,.wearing surface

Base

Subbase

/ / / / / / / Subgrade

I Flexible Pavement structure

Fig. 1. Typical flexible pavement.

properties to meet the requirements for use as a base course. Some typical materials used in the base course are gravel, crushed rock and cement stabilised granular materials. A subbase of granular material or stabilised material may be used if the subgrade soil is extremely weak, or where there exists suitable subbase materials that are more economical than the higher quality base materials. The subgrade is usually the natural material located along the alignment of the pavement and serves as the foundation of the pavement structure. In some cases it may be necessary to compact or treat the subgrade material to achieve the necessary strength requirements. The general methodology involved in pavement design essentially involves: selecting the surface and base materials, determining the minimum thickness required, and carrying out economic evalua- tion of the alternative designs to select the best solution.

In the NAASRA approach, a mechanistic design procedure is adopted in which the pavement is assumed to be subdivided into horizontal layers of elastic anisotropic materials. Linear elastic analysis is carried out to determine the induced stresses resulting from the application of a wheel load. Since excessive stresses will cause excessive cracking and excessive permanent deformation, the design criteria are based on limiting the horizontal and vertical strains in the pavement materials. Successful and cost-effective pavement designs requires a knowledge of the properties of the pavement materials and subgrade, the traffic loading, and the stresses acting on the pavement. The following is an outline of the NAASRA methodology for assessing the adequacy of a pavement design.

2.1 Subgrade characterisation

The support provided by the subgrade is influential in determining the thickness of the pavement layer. The subgrade characterisation involves the determination of the stiffness properties of the subgrade material, namely, the vertical and horizontal elastic moduli and the Poisson's ratio, from laboratory and/or field tests.

The vertical modulus can be obtained directly from the laboratory triaxial test or indirectly from the laboratory California Bearing Ratio (CBR) test. Where an existing pavement has similar subgrade soil conditions, field testing can be carried out using the in-situ CBR test, the cone penetrometer test and/or the plate loading test. Empirical relationships are used to determine the vertical

Table 1. Typical empirical CBR values

Subgrade material CBR values (%)

Well Poorly drained drained

Highly plastic clay (CH) 5 2-3 Silt (ML) Silty clay (CL) 6-7 4-5 Sandy clay (SC) Sand (SW, SP) 15-20 - -

modulus from the laboratory CBR test and the field testing methods. Similarly, the horizontal modulus is determined from an empirical relationship with the vertical modulus. The Poisson's ratio is usually obtained from correlations with the soil classification. For the purposes of preliminary design or when reliable information is not available, empirical values based on local experience are often used. This usually involves a correlation between the soil type and the CBR value. Table I shows the suggested empirical values from NAASRA.

2.2 Pavement material characterisation

The pavement material can be categorised as shown in Fig. 2.

The required design parameters are the thickness of each layer, the type of material, and the elastic stiffness properties of the material, and the elastic stiffness properties are obtained from laboratory tests (for example, repeated load triaxial test, flexural test and direct tension test). If no reliable information is available, more subjective characterisation based on empirical correlations, such as those shown in Table 2 for cemented materials, can be used. The most important factor affecting the elastic modulus of asphalt is the temperature. The correlations suggested by NAASRA for two of the different States in Australia are shown in Table 3.

I Pavement matcr~al type ]

quality H qualityl I base | rcx'k qL,~all,y

G M - o v c v g r a n u l a r rn~tcrlal

C M - ovcT s t t f T c c ~ n c n ; c d m a [ c r l a l

Fig, 2. Types of pavement materials.

Advisory expert system for flexible pavement design

Table 2. Typical values for cemented materials

49

Property Crushed rock Base quality Subbase (2-3% cement) gravel quality gravel

(4-5% cement) (4-5% cement)

Range of 3000-8000 3000-7000 1500-3000 modulus (MPa)

Typical vert. 5000 5000 2000 modulus (MPa)

Typical Poisson's 0-2 0.2 0.2 ratio

In addition, the location of the most critical strain depends on the material type. For asphalt and cemented materials, the horizontal tensile strain at the bottom of the layer is usually the most critical. The vertical compressive strain at the top of the subgrade is the most critical for the subgrade.

2.3 Traffic loading characterisation

The assessment of the traffic loading condition involves the determination of the loads applied by heavy vehicles. While cars and light commercial vehicles do influence the road capacity, their influence on the performance of the pavement is negligible. Because of the diversity in terms of magnitude and configuration of heavy vehicles, it is necessary to express the loading in terms of the number of equivalent standard axles (ESAs). The ESA is defined as the number of passes of the local standard axle load (8.2 tonnes) carried on a pair of dual tyres which would cause the same pavement damage as a single pass of the axle in question.

The main factors involved in the determination of the design ESAs are the axle type, the road category, and the pavement type. Figure 3 shows a condensed version of the inference net for the design ESAs.

The axle groups are divided into four types based on the axle load, the number of axles and the axle configuration:

• Single axle with single tyre (SAST) • Single axle with dual tyres (SADT) • Tandem axles both with dual tyres (TADT) • Tri-axles all with dual tyres (TRDT)

The five road categories are:

Table 3. Typical values for asphalt

Property Victoria Queensland

10°C 25°C 40°C 10°C 25°C 40°C

Typical vert. modulus (x 10MPa)

Typical Poisson's ratio

1780 600 157 i 130 300 87

0-4 0'4 0"4 0-4 0-4 0-4

• Class l - -Pr incipal avenues between capital cities (rural areas)

• Class 2--Principal avenues between capital cities and key towns (rural area)

• Class 3--Avenues between key towns and impor- tant centres (rural areas)

• Class 4--Principal avenues for massive traffic movements (urban areas)

• Class 5--Avenues which provide for traffic move- ments or distribute traffic to local streets

Two pavement types are considered:

• Flexible pavements containing one or more bound layers

I F,exa'blepavementtype [ I one bo~

annual ave. d axles by type load (AAD'I]

numbcr of

ddawnin~ion of Design ESA J

Fig. 3. Traffic data inference net.

50 A.T.C. Goh

Table 4. Recommended factors for Victoria

Road Factor Fi Class

& ~ F,

1 0"6 0'5 0"9 0"8 2 0"4 0"3 0"7 0"4 3 0-4 0"4 0"7 0"4 4 0"4 0-3 0-6 0"4 5 0.3 0-2 0"7 - -

• Flexible pavements consisting of unbound gran- ular materials

There are three alternative procedures for calculating the characteristics of the traffic data:

• The annual average daily number of axles by type and load method (AADTL)

• The annual average daily number of axles by type method (AADT)

• The annual average daily traffic plus percentage commercial vehicles method (AADTCV).

For example, in the AADT method, the design ESAs is calculated by first considering the initial daily ESAs (ARE), which is determined from the following:

ArE = NAIFI + NA2F 2 + NA3F 3 + NA4F 4 (1)

where NAt, NA2, Nm and NA4 are the daily number of each of the types of axle groups SAST, SADT, T A D T and TRDT, respectively, and FI, F2, F3 and F4 are functions of the road category and State. For example, Table 4 lists the NAASRA recommended parameters for the State of Victoria.

The design ESAs is calculated as

Design ESAs = NEGF × 365 (2)

where GF is the cumulative growth factor. GF is a function of the design period of the pavement and the anticipated growth rate of the traffic capacity. For example, for a design period of 15 years and a growth rate of 4%, the suggested GF is 20-0. The NAASRA guidelines are shown in Table 5.

A detailed description of the procedures are found in NAASRA. 3

Table 5. NAASRA recommended growth factors (GF)

Design Growth rate (% pa) period (years) 0 2 4 6 8 10

5 5 5.2 5.4 5'6 5'9 6"1 10 10 10-9 12.0 13.2 14.5 15"9 15 15 17"3 20"0 23"3 27-2 31'8 20 20 24-3 29-8 36"8 45-8 57.3 25 25 32"0 41.6 54"9 73'1 98.3 30 30 40.6 56"1 79'1 113"3 164-5 35 35 50'0 73"7 111"4 172"3 271"0 40 40 60"4 95"0 154"8 259" 1 442-6

L~spaciag of wheels J

o ~ ty~ CL p~ssure

~,et el asphalt

granular material

cemented e2 material

Z A ~ 3 A subgrade

• 1 = tensile strain at bottom of

asphalt(horlz.) e2 = tensile strain

at bouom of cemented mat. (hodz.)

e3 = compressive strain at top of subgrade(verz)

I = l ikely location of critical strain

Fig. 4. Typical pavement model.

2.4 Pavement analysis

The response of the pavement material to traffic loads is determined by linear elastic analysis. The analysis is commonly carried out using the F O R T R A N computer program CIRCLY. It The program is based on linear elastic theory and is capable of modelling a multilayered anisotropic medium subject to multiple circular loads. In the analysis, the dual wheel loading is modelled by two circular and uniform vertical stresses as shown in Fig. 4. The contact stress is related to the tyre pressure and is usually in the range 550-700 kPa.

From the CIRCLY output, the strains at the relevant locations, as illustrated in Fig. 4, are analysed. For asphalt or cemented material, the tensile strain at the bottom of the layer is the most critical. For subgrade material, the most critical is the compressive strain at the top of the subgrade.

The allowable number of load repetitions N before unacceptable cracking occurs is determined from the critical strain information obtained from the CIRCLY analysis. Some typical empirical relationships suggested by NAASRA are:

Asphalt

U = { [6918 (0-856 V B + 1.08)/(S°'36et)] } (3)

Cemented material

N = (280/e2) ~8 (4)

Subgrade

N = (851 l/e3) TM (5)

where et, e2, e3 = critical strains (in microstrains) as defined in Fig. 4, N = allowable number of load repetitions, VB = % voi. of bitumen in the asphalt, and S = stress-strain modulus (in MPa) of the mix.

A design is considered acceptable if the allowable number of standard axles (N) exceeds the design number of standard axles (Design ESAs). If this criterion is not met, then a new trial pavement is selected, and the entire design procedure is carried out again.

Advisory expert system for flexible pavement design 51

3 ARCHITECTURE OF PAVEDKB

The complexity of the design algorithm in which numerous combinations of pavement material types and tratEc data are possible, and the variation of many of the empirical correlations with the region or state suggests that an expert system approach could be useful.

The backward chaining expert system shell CRYS- TAL, t2 which runs on IBM compatible microcomputers, was used in the development of PAVEDKB. This shell was adopted mainly because of its availability and user- friendliness. It provides excellent colour graphics dis- plays, multiple windows and multiple choice menus and is capable of capturing graphics created using other graphics program as well as interfacing with external programs.

PAVEDKB was designed as a highly interactive microcomputer program in which the user is required to respond to a series of questions about the subgrade, pavement materials and design traffic. The user is guided through a series of questions that require a Yes/No response, a numerical response or a menu selection. Impact or 'what if' studies, can also be carried out without the user having to reenter all the input data. The Explanation Facility embedded within CRYSTAL has the capability to respond to queries as to WHY certain results are derived as well as to provide a trace of the inference logic. Additional Help Screens are also provided throughout the consultation process.

The general structure of the prototype expert system PAVEDKB is shown in Fig. 5. In the development of the expert system, a modular approach was adopted in which the knowledge-base of each subgoal, namely, the determination of the subgrade, the pavement material, the traffic loading and the pavement analysis, was developed separately before being integrated together. The domain knowledge of each of these modules essentially follows the NAASRA methodology described earlier. When site specific data are unavail- able, PAVEKB provides subjective guidelines for the characterisation of the subgrade, pavement materials and traffic data.

These submodules are instantiated through the menu screen shown in Fig. 6. Some of the rules activate commands to write the relevant data to an ASCII file which is then used as the input data file for the CIRCLY program. Some samples commands are listed:

Test: ASopennew("B:Circly.cin") Test: ASwritenum(Vert_mod,15,1) Test: ASwritenum(Vert_poi, 10,2) Test: ASnewline0

The knowledge-base then activates and runs the CIRCLY program and extracts the relevant critical strain data from the output file created by the CIRCLY analysis.

Material type I Material prop. Critical pmition

Pavement type [ Tr~/~c data I Axle load [ Design period I

[Feedback ] Growth period [ ESA.t I

l + I Pavement data

Field testia8 Dcailn too/meg oo¢ltc~t

I I cBR subgn,~ prop.

I Ci~:ty analystia I

Display coo¢lusion I Failta'e criteria I

Fig. 5. Architecture of PAVEDKB.

A condensed version of the inference net for the subgrade characterization submodule is presented in Fig. 7, PAVEDKB provides step-by-step procedures to guide the user through the various testing procedures as well as the methods of determining the design moisture content (DMC) at which testing is to be carried out. Tables and graphs of empirical correlations of the various labora- tory and field testing parameters are also provided to the user if required. The graphs were created using a graphics program, and the screens were captured using CRYS- TAL's screen capturing procedures.

Fig. 6. PAVEDKB menu screen.

52 A.T.C. Gob

[ T©fling equipment ] available ?

Yes ~, ~, ~o

Comparison with I existing mad I Use empirical correlations

I Determine design I moisture content (DMC)I

load test

Compare if appropriate 1

Select design I parameters

Fig. 7. Subgrade inference net.

Figures 8 and 9 show condensed versions of the inference nets for the pavement material characterisation and flexible pavement analysis sub-modules, respec- tively. The traffic data characterisation submodule has been described in detail in the previous section and presented in Fig. 3.

Some typical user input screens are shown in Fig. 10. A typical Help screen to assist the user to correlate the static cone data with the CBR value created, using a graphics program and captured using CRYSTAL's screen captur- ing procedures, is shown in Fig. 11. The pavement is considered acceptable if the allowable number of standard axles exceeds the design number of standard axles. A typical output screen as shown in Fig. 12 is then displayed. The screen similar to that shown in Fig. 13 is displayed if the failure criterion is not met. The user then has the option of modifying some of the pavement material properties and reanalysing the problem.

4 S A M P L E RULES

For brevity, the detailed listing of the entire knowledge-

I nput number of layers

r nput thickness I of each layer

Pavement material I type of each layer

Cemented Unbound material granular

material

Cnlical sCtain [ values location I

Fig. 8. Pavement material inference net.

base of PAVEDKB is not presented in this paper. Some sample rules (modified for the purpose of presentation) from the traffic loading knowledge-base are shown below. The rules essentially follow the inference net shown in Fig. 3.

% bitumen]

I I F SA~[ j

E cnlical slraln type I

I I [ ma' ° l ] [ J

microstrain ~ I P from CIR('I.Y oulpUl

Test for r~flure I

Fig. 9 Pavement analysis inference net.

Advisory expert system for flexible pavement design 53

Fig. 12. Typical output screen for successful pavement.

Else I f pavemen t type is unbound granular mater ial Then traffic da ta = required And traffic selection me thod 2 = required

Rule: /.]'traffic da ta = required Then Display Menu: traffic data type

Fig. 10. Typical input screens.

Rule: I f design traffic = required Then Initialise da ta And pavemen t type = required And pavement selection = required

Rule: I f pavement type = required Then Display Menu: pavement type

The pavement type comprises:

one or more bound layers unbound granular material

Rule: I f pavemen t selection = required And pavemen t type is one or more bound layers Then traffic data --- required And traffic selection me thod 1 = required

The traffic data type is:

Annual ave. daily no. of axles by type and load (AADTL)

Annual ave. daily no. of axles hy type (A:ADT) Annual ave. daily traffic plus % commercial

vehicles (AADTCV)

Rule: I f traffic selection me thod I = required And traffic da ta type is A A D T L Then Display Form: Inpu t - n u m b e r o f load cases And type and load 1 = required Else I f traffic da ta type is A A D T Then Display Menu: road classification

The road classification is:

Class I (prin. ave. between capital cities - rural areas) Class 2 (prin. ave. bet. capital cities & key towns -

rural areas) Class 3 (ave. bet. key towns & impt. centres - rural

areas) Class 4 (prin. ave. for massive traffic movements - urban

areas) Class 5 (ave. which provide for traffic movements or

distribute traffic to local streets - urban areas)

Fig. 11. Typical Help screen. Fig. 13. Typical output screen for pavement failure.

54 A.T.C. Goh

And type l = required Else I f traffic data type is AADTCV Then Display Menu: road classification (as above) And commercial vehicle 1 = required

Rule: I f type l = required And road classification is Class 3 Then Display Form: Input - SAST, SADT, TADT, T R D T And Assign: NE = SAST*0.4 + SADT*0.4 + TADT*0.7 + TRDT*0.4

And Display Form: Input - % traffic survey in design lane "rP

And Display Form: Input - cumulative growth factor GF (Help Screen shown) And Assign: ESA_Asphalt = NE* 1" l*365*GF*TP/100 And Assign: ESA_ Subgrade = NE* 1.1 * 365*GF*TP/100 And Assign: E S A _ C e m e n t e d _ M a t = NE*20*365*GF*TP/100

And Display Form: Output - ESA

Th e Design n u m e r of S tanda rd Axles for:

A S P H A L T : : ESAs =

C E M E N T E D M A T E R I A L : ESAs =

S U B G R A D E : ESAs =

5. EXAMPLE OF CONSULTATION

The following is a sample consultation session with PAVEDKB. The format has been modified for the purpose of presentation. For brevity, the Explanation and Help screens are not presented.

Subgrade module

Is Testing Equipment Available (Yes/No)? No Use Empirical Values (Help screen displayed). Input assumed CBR and Poisson's ratio.

CBR = 14 Poisson's ratio = 0-35

DISPLAY SELECTED DESIGN PARAMETERS: Vertical modulus = 140.0 MPa Horizontal Modulus = 70.0 MPa Vertical Poisson's Ratio = 0"35 Horizontal Poisson's Ratio = 0-35 Shear Modulus = 104-7 MPa

Layer 1

Layer 2

Layer 3

Measured values used (Menu selection) Material Type is Asphalt surface (Menu) Vertical Elastic Modulus = 2800 MPa Poisson's ratio = 0-45 Measured values used (Menu selection) Material Type is Crushed Rock (Menu) Vertical Elastic Modulus = 420 MPa Poisson's ratio = 0-30 Measured values used (Menu selection) Material Type is Cemented material (Menu) Vertical Elastic Modulus = 2500 MPa Poisson's ratio = 0.24

Specify Position of Critical Strain (Menu and Help screens displayed). Number of positions = 3 Position 1 = Tensile strain at bottom of asphalt Depth = 75 mm Position 2 = Tensile strain at bottom of cemented mat Depth = 425 mm Position 3 = Compressive strain at top of subgrade Depth = 425 mm

Traffic loading module

Select type of Pavement (Menu screen displayed) Pavement type = One or more bound layers What Traffic Data is Available? (Menu screen displayed). Data Type = Annual Average daily number of axles by type (AADT) Road Class = Class 3 (Menu selection)

Input Number of Standard Axles Single axle, single tyre (SAST)= 7 Single axle, dual tyre (SADT) = 13 Tandem axle, dual tyre ( T A D T ) = 9 Tri-axle, dual tyre ( T R D T ) = 22

Percentage of Traffic Survey Data in Design Lane = 50% Cumulative Growth Fac tor=29 .8 (Help screen dis- played).

DISPLAY RESULTS: Design ESAs for Asphalt is 1.38E05 Design ESAs for Cemented material is 2.51E06 Design ESAs for Subgrade is 1.38E05

Pavement analysis module

Pavement materials module

Number of Pavement Layers = 3 Depth of pavement layer 1 = 75 nun Depth of pavement layer 2 = 200 mm Depth of pavement layer 3 = 150 mm

Specify Material Type for Each layer and Elastic Stiffness Properties (Measured or empirical values)

Number of strain positions to be considered = 3 (Help screen displayed).

Tensile Strain at bottom of asphalt from CIRCLY analysis = 182-9 micro-strains % of bitumen in asphalt = 7*/. Design speed = 100 km]h Asphalt material well 'succeed' as Allowable ESA of 7.09EW5 > Design ESA of 1.38E05

Advisory expert system forflexible pavement design

Tensile Strain at bottom of cemented material from CIRCLY analysis = 70-5 micro-strains Type of material = loam (Menu selection) Subgrade material will 'succeed' as Allowable ESA of 6"05El0>Design ESA of 2.51E06

Compressive Strain at top of subgrade from CIRCLY analysis=209.7 micro-strains Subgrade material will 'succeed' as Allowable ESA of 4.11E11> Design ESA of 1.38E05

6 DISCUSSION

The NAASRA design methodology for pavement design relies heavily on the use of the F O R T R A N computer program CIRCLY for the determination of the strains in the pavement structure. The use of CIRCLY generally requires specialist expertise and familiarity with the software. In addition, it is not very user-friendly, and the generation of the input file data can be cumbersome. PAVEKB can be considered to be an intelligent front- end processor (IFE) for CIRCLY since it accomplishes the tasks required of an IFE t~ of:

• carrying out a dialogue with the user • producing a specification of the problem • using the specification to generate instructions for

CIRCLY • interpreting the output from CIRCLY • relaying answers to the user as part of the

continuing dialogue

In addition, PAVEKB incorporates engineering judgement in relation to the selection of the appropriate data for traffic characterisation as well as for subgrade and pavement material characterisation where site specific data are not available. It is envisaged that refinement of the knowledge base will embody further specialised knowledge related to the characterisation of the traffic, subgrade and pavement material as the software is adapted to suit local and regional transporta- tion agencies. The structure of the expert system in which the knowledge base is separated from the inference engine, and the use of natural language processing enables PAVEDKB to be modified/expanded and amplified more easily than conventional programming techniques.

PAVEDKB can assist in the decision making process since it enables a more rapid assessment of the pavement structure to be carried out. This will also permit designers to explore a more extensive range of design options by quickly testing out variations in the design. In addition, the transparency of the software should assist designers to avoid making errors in the design.

The use of the expert system shell CRYSTAL was found to be suitable for the development of PAVEDKB. The time required to master the essential features of

55

CRYSTAL was minimal- -no more than that required to learn to use a spreadsheet. One major limitation of CRYSTAL was in its screen capturing program, which only permitted graphics, such as the screen shown in Fig. 1 I, to be produced in low resolution (CGA) mode. Other attractive features not available on this version of CRYSTAL, such as object-oriented representation, hypertext and mouse control, would have been useful.

7 CONCLUSION AND RECOMMENDATION

The development of this prototype indicates that an expert system approach to pavement design is feasible. PAVEDKB can assist in the decision making process by enabling a more rapid assessment of alternative pave- ment design solutions to be carried out.

The structure of the expert system in which the knowledge base is separated from the inference engine, and the use of natural language processing, enables PAVEDKB to be modified/expanded and amplified more easily than conventional programming tech- niques. This will enable customisation of PAVEDKB to suit local conditions to be carried out readily.

The current version of PAVEDKB is limited to the design of flexible pavements. Further work will involve expanding and refining the knowledge base to incor- porate rigid pavement design as well as an optimal pavement design algorithm.

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56 A.T.C. Goh

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