2005

An expert system for mix design of high performance concrete

Muhammad Fauzi Mohd. Zain*, Md. Nazrul Islam1, Ir. Hassan Basri2

Department of Civil and Structural Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia,

43600 UKM Bangi, Selangor Darul Ehsan, Malaysia

Received 5 March 2004; received in revised form 20 August 2004; accepted 14 October 2004

Available online 10 December 2004

Abstract

This paper describes a prototype expert system called HPCMIX that provides proportion of trial mix of High Performance Concrete (HPC)

and recommendations on mix adjustment. The knowledge was acquired from various textual sources and human experts. The system was

developed using hybrid knowledge representation technique. It is capable of selecting proportions of mixing water, cement, supplementary

cementitious materials, aggregates and superplasticizer, considering the effects of air content as well as water contributed by superplasticizer

and moisture conditions of aggregates. Similar to most expert systems, this system has explanation facilities, can be incrementally expanded,

and has an easy to understand knowledge base. The system was tested using a sample project. The system’s selection of mix proportions and

recommendations regarding mix adjustment were compared favourably with those of experts. The system is user-friendly and can be used as

an educational tool.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: High performance concrete; Mix design; Mix adjustment; Expert systems; Knowledge-based systems; Hybrid knowledge representation

1. Introduction

The selection of mix proportions is the process of

choosing suitable ingredients of concrete and determining

their relative quantities with the object of producing as

economically as possible concrete of certain minimum

properties, notably strength, durability, and a required

consistency [1]. Because the ingredients used are essentially

variable and many of the material properties cannot be

assessed truly quantitatively, selecting proportions for

concrete can also be defined as the process of finding the

optimum combination of these ingredients on the basis of

some empirical data as stated in relevant standards,

experience, and some rules of thumb [2].

Concrete mix design involves complicated issues, and

the correct ways to perform this can be achieved with

0965-9978/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.advengsoft.2004.10.008

* Corresponding author. Tel.: C603 89216223; fax. C603 89216147.

E-mail addresses: [email protected] (M.F. Mohd. Zain),

[email protected] (M. Nazrul Islam), [email protected]

(I. Hassan Basri).1 Tel.: C603 89216819; fax: C603 89216147.2 Tel.: C603 89216100; fax: C603 89216147.

experts’ advice and experience [3]. Mix design of High

Performance Concrete (HPC) is more complicated because

HPC includes more materials, like superplasticizer and

supplementary cementitious materials (e.g. silica fume, fly

ash, fillers, etc.). In addition, maintaining a low water-

binder ratio with adequate workability makes the design

process more complicated. Traditionally, experienced civil

engineers, largely based on their experiential knowledge,

do the job of mix design [4]. However, experts are not

always available, nor do they always have time to consult

all possible references, review available data, and so on.

Some companies do not have personnel with the experi-

ence to make necessary decisions regarding concrete mix

design. The conventional computer programs are useful

only in manipulating the numerical data and providing

mathematical reasoning for the final selection. They lack

the intuitive reasoning based on heuristic knowledge such

as experience and rules of thumb [5]. Many factors

influence concrete mix design, and their mutual relation-

ship is so complicated that it is impossible to formulate

mathematical models to express their mutual actions and

reactions [6]. In addition, adjustments of trial mixes are

always performed by taking into account the information

Advances in Engineering Software 36 (2005) 325–337

www.elsevier.com/locate/advengsoft

http://www.elsevier.com/locate/advengsoft

M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337326

from concrete quality tests, experts’ advice and experience.

It is believed that the problem of mix design and

adjustment of HPC can be alleviated if the engineer’s

knowledge can be augmented with some ‘expert system’

for affirming his judgment.

This paper describes a prototype expert system called

HPCMIX. The purposes of developing HPCMIX were to

improve the process of selecting and proportioning HPC

constituents and to make the knowledge of HPC easily

available to the concrete industry. It is also capable of

diagnosing causes of mix performance failure and giving

recommendations on corresponding performance

adjustment. However, like other expert systems, the

developed expert system will serve as a decision support

system; it will not replace completely human expert’s

decision making.

2. Expert systems and concrete mix design

An expert system is defined as ‘a computer program

designed to model the problem-solving ability of a human

expert’ [7]. It utilises observed or available information to

produce ‘high grade’ knowledge and solves problems by

qualitative reasoning ‘using the heuristic knowledge of the

human expert’ [8]. Expert systems are most useful when the

knowledge is based on heuristics, which is often the case in

concrete mix design. Since concrete mix design and

adjustments are somewhat complicated, time-consuming

and tedious tasks, and also because it is not always possible

to be helped by the experts, there were some efforts to

develop expert system for concrete mix design. These

systems [2,3,9,10] give proportion of concrete mix

especially for normal concrete. A brief review of these

systems is available elsewhere [11]. Most of these systems

are rule-based systems, work using DOS operating system

and do not consider the cost of concrete mix selected. None

of these systems can diagnose causes of performance failure

of trial mix and give recommendations on corresponding

performance adjustment. Most importantly, they do not

consider the criteria of mix design for HPC such as

maintaining a low water-binder ratio and use of super-

plasticizer, silica fume, and so on. Therefore, these systems

cannot be used for mix design and adjustment of high

performance concrete.

3. Development of the HPCMIX

3.1. Knowledge acquisition

Knowledge for the HPCMIX was acquired from text-

books and manuals written by experts and related

professional institutions [1,12–21], research papers from

journals and conference proceedings [3,22–29] and experts

involved in concrete production. Thus knowledge was

acquired by text analysis (i.e. collection of knowledge from

the literature) and interviewing experts. Only unstructured

interviews of several experts involved in teaching, research

and consultation of concrete production were performed

in this project. Experts were asked to describe their

knowledge about the selection of concrete proportions,

diagnosing the problems in mix design and adjustments,

and their solutions. However, the main source of

knowledge was the literature mentioned above. By

analysing the knowledge from these sources, a more

objective perspective of the most appropriate expertise was

achieved, instead of being restricted to a single view

preferred by a particular expert. It may be relevant to

mention here that acquiring knowledge from these sources

was felt to be the most difficult and time-consuming task in

the prototype development process.

The mix design procedure for HPC developed by Aitcin

[15] was used as the mix design procedure for the HPCMIX

because of its wide acceptability among the experts in

Malaysia (Alternatively, in future, any other state-of-the-art

mix design method can be added to the system as a new

module without affecting overall performance of the

system). The Aitcin method follows the same approach as

ACI Committee 211 [18]. It is a combination of empirical

results and mathematical calculations based on the absolute

volume method. A flow chart of this method is presented

latter in this paper (Section 4.4). The procedure is initiated

by selecting five different mix characteristics or materials

proportions in the following sequence: water-binder

ratio, water content, superplasticizer dosage, coarse

aggregate content and entrapped air content. The suggested

water-binder ratio is obtained from a ‘compressive strength

vs. water-binder ratio’ graph for a given 28-day compressive

strength. The mixing water content is determined on the

basis of saturation point of superplasticizer. The super-

plasticizer dosage is deduced from the dosage at the

saturation point. The coarse aggregate content is obtained

as a function of the typical particle shape. The method

suggests using 1.5% as an initial estimate of entrapped air

content, and then adjusting it on the basis of the result

obtained with trial mix.

The flow diagram developed for the acquired knowledge

regarding mix performance adjustment is shown in Fig. 1.

The diagram shows that three criteria were considered for

judging the performance of a mix, i.e. strength, workability

and durability. For example, if a mix fails in strength

performance, then information is required about test results

regarding any of the following to find out possible causes of

performance failure: (i) fracture pattern, (ii) bond failure

pattern, (iii) passage of fracture surface, and (iv) effect of

water-binder ratio. On the other hand, if the workability

performance is inadequate then it is required to know which

workability performance of the following is inadequate:

(i) rapid slump loss, (ii) low slump, or (iii) inadequate

workability. The flow diagram developed for the purpose of

cost estimation of a designed mix is shown in Fig. 2.

Fig. 1. Flow diagram of mix performance adjustment.

Fig. 2. Flow diagram of cost estimation.

M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 327

The figure shows that unit cost of ingredients, mix

composition and batch size are the required information

for cost estimation of a concrete mix.

3.2. Knowledge representation

Through the efforts of researchers in artificial intelli-

gence, a number of effective ways of representing knowl-

edge in a computer were developed [7]. The selection from

these knowledge representation techniques depends on the

nature of the expertise to be computerized, as well as the

practical capabilities and facilities of the expert system tool

used [30]. In developing HPCMIX, a hybrid approach of

knowledge representation (i.e. rule and frame systems) was

followed using Kappa-PC expert system shell [31]. Thus,

the domain of mix design of HPC was modelled using

object-oriented approach and production rules.

As an illustration of object-oriented approach, Fig. 3

shows the object hierarchy of Binder class. It consists of two

subclasses namely Cement and SupplCemMats (Supplemen-

tary Cementitious Materials). Each of these subclasses

includes several instances. For example, SupplCemMats

subclass consists of SilicaFume, FlyAsh, GGBS (ground

granulated blast-furnace slag), RiceHuskAsh and Others

instances. The attributes of objects were defined as object

slots. Slots can be thought of as descriptions of a particular

object. They add detail structure, list attributes or properties

which can be single or multiple-valued, textual strings or

numeric, or even Boolean. Slot values can be pre-defined,

restricted to a range or set of pre-specified possible values,

user-defined or determined from user consultations with

Fig. 3. Object hierarchy of the Binder class.


the system. For example, the slots of the instance FlyAsh

(Fig. 3) includes, among others, content in percent, specific

gravity and content in kg/m3 (kilogram per cubic metre).

Table 1 shows these slots and the corresponding slot values.

Interactions among objects, instructions from one object

to another, and processes of objects were codified in the form

of methods, functions and rules. The first approach involves

enhancing objects so that they represent the behaviour of the

things to which they correspond. Methods are written in KAL

(Kappa-PC Application Language) and stored within the

object. The next approach to representing processes involves

functions. Functions are also written in KAL and can either

be user-defined or built-in the system. Kappa-PC provides a

library of over 300 functions that allow for the manipulation

of its knowledge base. The third approach involves using

rules to represent the relationship between causes and effects

which specifies the conditions under which a particular

action or inference can occur. The syntax for methods,

functions and rules is identical; therefore, the same syntax

can be used to add an object, to write a method or to create a

rule. An example of a simple rule written for the coarse

aggregate content of the mix design module is shown below.

If:

Table 1

Descripti

Instance:

Slot Nam

ContentP

SpecificG

ContentK

Shape of the coarse aggregate is cubic

Then:
Coarse aggregate content of the mix should be
1100 kg/m3.

In KAL format, this rule was written as:

If:
CoarseAgg: ShapeOfAgg #ZCubic
Then:
CoarseAgg: ContentKgPerM3 #Z1100;
on of an Instance using Slots

FlyAsh, Parent Class: SupplCemMats

e Slot Value Comment

ercent 10.00 User defined value

ravity 2.50 User defined value

gPerM3 53.19 System derived value

Where, CoarseAgg is an instance representing coarse

aggregate; ShapeOfAgg is a slot of CoarseAgg instance

representing the shape of the coarse aggregate; Cubic is the

value of the slot ShapeOfAgg representing that the shape of

the coarse aggregate is cubic; and so on. There are two

approaches for evaluating production rules: backward

chaining and forward chaining [7,32–34]. The HPCMIX

uses a forward-chaining or data-driven inference

mechanism.

4. Knowledge base modules of the HPCMIX

The HPCMIX knowledge base consists of three design

related modules namely Mix Design, Mix Performance

Adjustment and Cost Estimation modules and an accessory

module named General Information module. The modules

can be accessed from the main interface window shown in

Fig. 4. A brief description of each of the above modules is

given in the following sections.

4.1. Mix Design module

The objective of Mix Design module is to proportion

HPC mixes. It consists of four submodules namely First

Trial Batch, Trial Batch for Laboratory, One Cubic Metre

SSD Composition (SSD stands for saturated-and-surface-

dry) and Batch Composition as shown in Fig. 5. The First

Trial Batch submodule computes proportions of concrete

mixes according to the data supplied by the user. It gives

composition of one cubic metre of concrete for field

conditions of aggregates. The Trial Batch for Laboratory

submodule helps in computing quantities for making

concrete samples for laboratory testing. This gives the

user opportunity to test mix design results in a laboratory for

desired performance requirements for a small amount of

proportioned ingredients. The One Cubic Metre SSD

Composition submodule calculates proportions for one

cubic metre of concrete for SSD conditions of aggregates.

The Batch Composition submodule activates a function to

calculate amounts for batch quantities for field conditions of

aggregates.

4.2. Mix Performance Adjustment module

The Mix Performance Adjustment module helps in

adjusting mix proportions after laboratory testing. The

interface window of this module is shown in Fig. 6.

Incorporating the knowledge that experts use in diagnosis,

the Mix Performance Adjustment module diagnoses

possible causes of performance failure of concrete mix

and recommends corresponding remedial measures. The

Quantitative Advice button (Fig. 6) of this module gives

specific quantitative recommendations on achieving various

performances of HPC mix. The module also displays

reasons for giving any recommendation.

Fig. 4. Main interface window of the HPCMIX.

Fig. 5. Interface window of the Mix Design module.


Fig. 6. Interface window of the Mix Performance Adjustment module.


4.3. Cost Estimation module

If the cost estimation is felt to be necessary, it can be

carried out using the Cost Estimation module. The first step

is to input unit cost of the proportioned ingredients. A

transcript image in the Cost Estimation interface window

helps the user by displaying information about the ranges of

unit cost of concrete ingredients. Although these values vary

from country to country, this information will help the user

in getting idea about approximate unit cost of ingredients.

After inputting the unit cost of ingredients, the user gets the

costs of one cubic metre of concrete and of a particular batch

size by pressing appropriate buttons.

4.4. General Information module

Fig. 7 shows the interface window of the General

Information module. The objective of this module is to

assist the user in the efficient utilisation of the HPCMIX

prototype. The module provides a user-friendly environ-

ment whereby the various guidelines and information are

available to the user. The User Guide button assists the user

in the efficient consultation process. The user gets advice on

the fundamentals of HPC and the approaches of mix design

and adjustment of HPC through HPC Technology and

Principle of Mix Design buttons of this module. A

knowledge dictionary is also available through Knowledge

Dictionary button to assist the user with unfamiliar technical

terms. This dictionary is also useful as an educational

feature. The dictionary includes: basic definitions of

concrete mix design, HPC and expert system; types of

tests for evaluation of fresh and hardened concrete; and

statistical measures used in assessment of concrete mix

design. In addition, a general conceptual flow diagram of

HPC mix design using Aitcin method (see Fig. 7) and

various photographs showing the testing of fresh and

hardened concretes are also accessible through appropriate

buttons of this module.

5. Case study and evaluation of the HPCMIX

The objective of this case study was to evaluate the

performance of the HPCMIX consultation process and

results when it was applied to a HPC mix. The case study

was carried out on an example mix design of HPC from a

classical textbook [15]. This example was selected because

the author of this textbook is considered as one of the well-

known experts in the domain of HPC. Another expert (Dr

Hilmi Mahmud, Associate Professor, University of Malaya,

Malaysia), who has been involved in teaching, research and

consultation in the production of concrete for past fifteen

years, was asked to perform concrete mix design based on

the information of this textbook. The results of the design

obtained from HPCMIX were compared with those

reported in this textbook (referred to as Expert-1) and

those calculated by Dr Hilmi Mahmud (referred to as

Expert-2).

Fig. 7. Interface window of the General Information module showing mix design flow diagram.


The user clicked on Mix Design button of the main

interface window (Fig. 4) for mix design purpose. This

opened the interface window of the Mix Design module

(Fig. 5). From that window, the user learned about the

functions of the buttons regarding mix design submodules

and got advice about each submodule by pressing on

appropriate Advice button. The input data, consultation

process and explanation of results of each submodule of the

mix design module as well as mix performance adjustment

module are described in the following sections. These

sections also include description about user-friendliness,

advantages, disadvantages and an overall evaluation of the

system.

5.1. First trial batch

Data for the first trial batch composition of the present

case study is described in Appendix A. In this example, a

100 MPa concrete mix was designed with Type I Portland

cement, a naphthalene-type superplasticizer, a dolomitic

limestone, a siliceous natural sand and silica fume.

For each concrete ingredient of Appendix A, a related

window appeared one after another as the user continued

with data input process. For example, window that

appeared for data input related to silica fume is shown in

Fig. 8. The system provided information about each

concrete ingredient similar to that shown for silica fume

(Fig. 8). Each of these windows also displayed several

buttons namely Data Input, Advice, Main Screen, Mix

Design Screen, Back and Exit. If the user wanted to input

data about an ingredient, he pressed on the Data Input

button. As a result, the system prompted the User Request

form such as shown in Fig. 9. This form displayed the

limits of the value of the desired input parameter. After

entering the value of the parameter, the user pressed on OK

button and, as a consequence, the window for the next

input parameter automatically appeared. If the user was not

sure about the value of the parameter, he could press on

Comment button (Fig. 9) for usual range of values as

shown in Fig. 10, which also gave the sources of

information. For the beginners or students of concrete

technology who are not familiar about the parameter may

press Advice button (see Fig. 8). This operation gives

detailed information about the parameter in the second

transcript image (see Fig. 8 for advice on silica fume).

Moreover, the beginners can access to knowledge

dictionary through Knowledge Dictionary button

(see Fig. 7). These facilities make the system very

user-friendly and suitable for using as an educational tool.

At the end of data input session, the Summary of Input

Data window appeared. From that window, the user could

verify input data. If there was any inconsistency, the user

could go back and modify it. After verifying input data, the

user could see design values (i.e. First Trial Batch

composition) by pressing on Final Design button. The

design values after moisture adjustment as proportioned by

the HPCMIX are shown in Fig. 11. This figure also shows

the key input parameters that were input by the user. From

this window, the user might request for explanation about

the mix design process, might go back to main screen or

Fig. 8. A typical window during data input process.


might exit the mix design session by pressing on appropriate

buttons.

Table 2 shows the comparison of the trial batching mix

design selected by the developed system and the experts. It

can be observed from Table 2 that the results of the three

mix designs are very close except cement and fine aggregate

contents. The cement content suggested by the system is the

lowest (i.e. 464.38 kg/m3) and that suggested by Expert-1 is

the highest (i.e. 470.00 kg/m3). This was because the system

maintained high precision in computation by considering up

to ten digits after decimal point. But Expert-1 rounded off

the figures to integer values for convenience. For example,

total binder content was rounded off from 518.5 to

520 kg/m3 and silica fume content was rounded off from

Fig. 9. A typical User Request form during data input (after pressing Data

Input button of Fig. 8.

51.85 to 50 kg/m3 by Expert-1. On the other hand, cement

content suggested by Expert-2 was of intermediate value

(i.e. 466.67 kg/m3) because he considered only two digits

after decimal point in his calculation. Due to the variation in

cement content, the variation in fine aggregate content was

also observed among the three mix designs

(i.e. 777.51 kg/m3 by HPCMIX, 772.00 kg/m3 by Expert-1

and 775.17 kg/m3 by Expert-2) in order to maintain the total

displaced volume according to absolute volume method.

However, Expert-2 indicated that the differences among the

three mix designs were not critical. Expert-2 also indicated

that the concrete proportions selected by the system were

accurate enough for the first trial batching. It may be

relevant to mention here that, for a particular mix design,

there are always several answers, which can satisfy the

requirements of the specification [3]. Thus the concrete

proportions selected by the system were accurate enough for

Fig. 10. A typical explanation window during data input (after pressing

Comment button of Fig. 9.

Fig. 11. Window showing composition and key data of First Trial Batch submodule.


the first trial batching. The developed system is, therefore,

capable of proportioning the first trial batch of HPC mix

with acceptable accuracy.

5.2. Trial batch for laboratory

The calculated mixture proportions should be checked by

means of trial batches prepared and tested for the intended

performance requirements [15]. These tests and observation

on the designed mix are usually performed in the laboratory

by using a small portion of the designed mix, i.e. by

laboratory trial batch. For the purpose of validation of

Table 2

Comparison of results of First Trial Batch

Item HPCMIX Expert-1a Expert-2b

Water-Binder

Ratio

0.27 0.27 0.27

Water (l/m3) 123.38 124.00 123.40

Cement (kg/m3) 464.38 470.00 466.67

Silica Fume

(kg/m3)

51.60 50.00 51.85

Coarse Aggre-

gate (kg/m3)

1066.40 1066.00 1066.40

Fine Aggregate

(kg/m3)

777.51 772.00 775.17

Superplasticizer

(l/m3)

10.66 10.70 10.72

Air Content (%) 1.50 1.50 1.50

a Expert-1: Aitcin [15] (see reference).b Expert-2: Dr Hilmi Mahmud, University of Malaya, Malaysia.

the system, data used for an example laboratory testing is

described in Appendix B. The results computed by the

system and those of the experts are compared in Table 3. It

can be observed that the proportions selected by the

HPCMIX were close enough to those selected by

the experts. The highest variation was observed in

fine aggregate content, which was only 0.69 kg/m3

(i.e. 72.42–71.73Z0.69). According to the expert’s opinion,

the variations among the three mix designs were negligible.

Thus the present system can be used for the calculation of

laboratory trial mix of HPC with acceptable accuracy.

Table 3

Comparison of results of Laboratory Trial Batch


Water-Binder

Ratio

0.27 0.27 0.27

Mixing Water

(litre)

11.49 11.53 11.42

Cement (kg) 43.26 43.70 43.18

Silica Fume

(kg)

4.81 4.70 4.80

Coarse Aggre-

gate (kg)

99.33 99.10 98.68

Fine Aggregate

(kg)

72.42 71.80 71.73

Superplasticizer

(litre)

0.99 0.99 0.99

Air Content (%) 1.50 1.50 1.50


Table 5

Comparison of results of Batch Composition


Water-Binder

Ratio

0.29 0.29 0.29

Mixing Water

(litre)

875.08 872.00 875.12

Cement (kg) 3600.00 3600.00 3600.00

Coarse Aggre-

gate (kg)

8400.00 8400.00 8400.00

Fine Aggregate

(kg)

6120.00 6120.00 6120.00

Superplasticizer

(litre)

64.00 64.00 64.00



5.3. One cubic metre SSD composition

This submodule can be used to compute composition of

one cubic metre of concrete for SSD conditions of

aggregates using the data of laboratory trial batch. It can

also be used for conversion of concrete composition from

field conditions of aggregates to SSD conditions of

aggregates. During computation, it takes into account

moisture conditions of aggregates in the bins as well as

water hidden in liquid superplasticizer. The data for a

sample example are presented in Appendix C. The results

are compared in Table 4 that shows good agreement

between the results of the HPCMIX and those of the

experts. The highest variation was only 0.88 kg/m3

(i.e. 1074.51–1073.63Z0.88) in coarse aggregate content.

The system gave proportions closer to those of Expert-2.

Expert-2 was consulted and he agreed that the variation was

negligible and the concrete proportions selected by the

system were accurate enough to make the trial batching.

These results serve as a means of verification of the

accuracy of knowledge base of the developed system.

5.4. Batch composition

This submodule computes batch composition for a

concrete construction project for field conditions of

aggregates. The system collects following data from the

user in order to compute batch composition: one cubic metre

SSD composition, moisture conditions of aggregates in the

field, and the expected batch size. It was assumed, for

example, that a concrete batch plant was to produce 8 m3 of

HPC on the basis of the SSD composition shown in

Appendix D. The user input those data by pressing

appropriate buttons of the Batch Composition interface

window. After the completion of data input, the user got

results of the batch composition for field conditions of

aggregates. The results are compared with those of the

experts in Table 5. Again, it can be seen that the results of

the HPCMIX compared well with those of the experts.

Table 4

Comparison of results of One Cubic Metre SSD Composition


Water-Binder

Ratio

0.27 0.27 0.27

Water (l/m3) 144.61 144.88 144.57

Cement (kg/m3) 478.70 478.35 478.72

Fly Ash (kg/m3) 53.19 53.15 53.19

Coarse Aggre-

gate (kg/m3)

1074.51 1073.63 1074.57

Fine Aggregate

(kg/m3)

723.65 722.84 723.72

Superplasticizer

(l/m3)

9.57 9.57 9.57

Air Content (%) 1.50 1.50 1.50


The only variation with the result of Expert-1 was observed

in mixing water requirement (i.e. 875.08–872.00Z3.08 l).

Expert-1 rounded off the water contributed by liquid

superplasticizer from 5.6 to 6.0 l in one cubic metre SSD

composition calculation. This excess water contributed by

superplasticizer (i.e. 6.0–5.6Z0.4 l) in one cubic metre

composition was multiplied by 8 for getting 8 m3 compo-

sition (i.e. 0.4!8Z3.2 l). On the other hand, this excess

water (i.e. 3.2 l) was subtracted from the required mixing

water and hence the value was less (i.e. 872 l instead of

875.2 l). Again Expert-2 was consulted and he agreed that

the performance of the HPCMIX in computing batch

composition was satisfactory. This comparison also gives

a positive indication of the accuracy of knowledge base of

the system.

5.5. Mix performance adjustment

The purpose of this module is to diagnose possible causes

of performance failure of a HPC mix and to recommend on

corresponding remedial measures in order to achieve the

desired performance. These features are demonstrated in

this portion of the case study. It was assumed, for example,

that the strength performance of the mix was not achieved in

the laboratory testing of the first trial batch. Careful

examination of the failure pattern of the test specimen

revealed that fracture surface passed through hydrated

cement paste. After getting these data from the user, the

inference engine of the HPCMIX forward chained through

its knowledge base and produced recommendation as stated

ahead (see Fig. 6): ‘Use lower water-binder ratio’. The

system also explained the reason for giving this recommen-

dation (see Fig. 6): ‘If the fracture surface passes almost

entirely through the hydrated cement paste around the

aggregates, a stronger concrete can be made with the same

aggregates by lowering further the water-binder ratio’. The

system also recommended, when the user pressed on

Quantitative Advice button, that increasing the 28-day

compressive strength of concrete by 1 MPa necessitate the

addition of approximately 8.66 kg/m3 of extra cement to


the mix. These recommendations and explanation exactly

matched with those of the experts [3,15] and these

recommendations were better than the recommendation

from any individual of these experts. Thus the performances

of the prototype to diagnose mix performance failure and to

give recommendation on corresponding performance

adjustment are considered to be satisfactory.

5.6. User-friendliness and the user interface

The HPCMIX user interface was designed for user

friendliness to enable its efficient utilization in the

following ways.

(i)
Due to the flexibility in determining flow of consul-
tation, the user may need to be advised on the most

appropriate sequence of design steps. In order to

achieve this goal, the User Guide button in the General

Information module gives brief information of the

overall consultation steps. On the other hand, the first

Transcript Image in the beginning of each consultation

session provides information regarding the steps to

follow for that session (for example, see Fig. 5). In

addition, Advice buttons are available in most of the

modules (see Fig. 5, for example) to guide the user

about design procedure. Moreover, each consultation

window contains some buttons to guide the user for the

next design steps.

(ii)
The advantages and disadvantages of each alternative
solution are made easily available to the user so that he

can interact with the HPCMIX with better perspective.

For example, the Advice button in the Binder Types

window during the data input for binder types opens

text information on binders (similar to Silica Fume

window in Fig. 8). It explains the advantages and

disadvantages of using cement only and cement and

pozzolans as binder. The user will then be able to have

a better understanding of the alternative use of these

binders.

(iii)
Each User Request form of data input has a Comment
button attached to it as described earlier (see Fig. 9).

This helps the user by expanding the meaning of a

question and thus aids the user in responding more

efficiently to the prompts of the design procedure.

(iv)
One of the distinguishing characteristics of an expert
system such as the HPCMIX is the transparency of its

reasoning process and knowledge base. This advantage

is available to the user through Explain button (for

example, see Fig. 11), which displays the rules that

have been used by the inference mechanism, thus

explaining the reasons for arriving at a particular

recommendation. Another example of explanation

facility is shown in the second transcript image of the

Mix Performance Adjustment module (Fig. 6), which

explains the reasons for giving any recommendation

regarding mix performance adjustment.

(v)
As mentioned earlier, the General Information module
contains a Knowledge Dictionary. Basic knowledge

about HPC, mix design and expert system is available

there. It also contains some statistical information. This

dictionary is very useful as an educational feature.

Moreover, concrete mix design flow diagram (see

Fig. 7) and several photographs are included in the

General Information module to enhance the edu-

cational performance of the HPCMIX.

5.7. Advantages, disadvantages and overall

evaluation of the system

It may be mentioned, by considering all these features,

that the consultation process of the HPCMIX is reason-

ably satisfactory and systematic. The flow of consultation

is flexible, allowing the user to reset data, to go back for a

new consultation, to review input values and other

procedures until he is satisfied with the results. The

ability of the HPCMIX to run using Windows operating

system, to give recommendation on possible causes of

performance failure of the mix, and the facility of

knowledge dictionary make this system superior to similar

other systems in the domain. The system-user interaction

is very interactive with explanatory facilities available

throughout the consultation session. Moreover, the system

gives information of data at every stages of data input,

which makes it superior to conventional programs of

concrete mix design. It has facilities like Explain, Advice

and Comment buttons as well, which make the system

very user-friendly.

The main disadvantage of the developed system is that it

is applicable for concrete compressive strength from 40 to

160 MPa for which Aitcin method is valid. Another

limitation of the system is that it is not platform

independent. The user will need a runtime version of

Kappa-PC in order to use the system. However, in future,

these limitations can be handled by incorporating another

state-of-the-art mix design method and by using another

state-of-the-art expert system shell if they are proved to be

useful.

In order for expert systems not to become obsolete, they

must be nurtured and kept current [35]. All expert systems,

the HPCMIX included, cannot claim completeness in their

knowledge bases; they are always subject to upgrading,

modification and correction. It should be recognized that

HPCMIX is a research prototype; and hence, it must be

refined and tested further for commercial use. The existing

knowledge base of the prototype can be improved by

refining, expanding, and reinforcing its knowledge base

using new findings as reported in literature or new

experience from domain experts. It must also be kept in

mind that, like other expert systems, HPCMIX will serve as

a decision support system; it will not replace completely

human expert’s decision making.


6. Conclusions

This paper presented a prototype expert system called

the HPCMIX developed for mix design of HPC. The

HPCMIX is capable of selecting proportions of mixing

water, cement, supplementary cementitious materials,

superplasticizer and aggregates, considering the effects of

air content as well as water contributed by superplasticizer

and moisture conditions of aggregates. Using an expert

system shell, HPCMIX was developed according to the

mix design method proposed by Aitcin. Alternatively, in

future, any other state-of-the-art mix design method of

HPC can be added to the system as a new module without

affecting overall performance of the system. In addition to

proportioning concrete mixes, the system is also capable

of giving recommendations on mix performance adjust-

ment. It was found that it is feasible, efficient and

effective, to use an expert system approach for the

proportioning of HPC mix. The ability of the system to

give comments and advice about an input data, together

with the facility of knowledge dictionary, makes this

expert system very useful for the educational environment.

Additional knowledge to expand the scope of the system

can be added without major modification of the structure

of the program.

The best approach to making a mix design of HPC, of

course, is to use proportions previously established for

similar concrete using the same materials. In addition,

rules-of-thumb and past experience should also be used,

wherever possible. Where such prior information is limited

or unavailable, the HPCMIX can be used to assist the user in

the mix design of HPC.

Acknowledgements

The authors would like to express sincere gratitude to

Universiti Kebangsaan Malaysia for providing the fund for

the research and MBT (Malaysia) Sdn. Bhd for the supply of

materials and technical support throughout the research

program. The authors would like to thank Dr Hilmi

Mahmud (University of Malaya, Malaysia) for his

invaluable input and patience.

Appendix A. Data for first trial batch

It was supposed that a 100 MPa concrete had to be made

with the following data [15]: a Type I Portland cement; a

naphthalene-type superplasticizer with a total solids

content of 40% and specific gravity of 1.21; a dolomitic

limestone having maximum sizeZ10 mm, specific gravity

(SSD)Z2.80, absorptionZ0.8%, field moistureZ0%, and

the shape of the particles can be described as between

average and cubic; a siliceous natural sand of

specific gravity (SSD)Z2.65, absorptionZ1.2%, and field

moistureZ3.5%. Silica fume at 10% replacement (of total

cementitious material) was to be used; its specific gravity

was 2.20. The dosage of solids superplasticizer at the

saturation point was 1.0%.

Appendix B. Data for laboratory trial batch

In order to test First Trial Batch of HPC mix, the

following specimens are needed [15]: three 100!200 mm

cylinders for tests at 1, 7, 28 and 91 days in compression;

three 150!300 mm cylinders for tests at 28 days in

compression; three 150!300 mm cylinders for tests

for elastic modulus at 28 days; and three beams 100!100!400 mm for tests for modulus of rupture at 28 days. A

slump test, an air content test and a unit mass test will be

done on the fresh concrete. Except the air content test, the

concrete used for these tests will be recovered. Knowing

that an air test needs 15 kg, the amount of concrete to make

this trial batch needs to be calculated, assuming 10% extra

materials to compensate for losses.

Appendix C. Data for one cubic metre SSD composition

A trial batch with an adequate consistency and adequate

initial and final slumps was made using the following

quantities of materials [15]: waterZ12 l, cementZ45 kg,

Fly ashZ5 kg, coarse aggregateZ100 kg, fine aggregateZ70 kg, and superplasticizerZ0.9 l. The air content of this

trial batch was 1.5%. The materials used to make this trial

batch had the following properties: coarse aggregate-


moistureZ0%; and fine aggregate-specific gravity

(SSD)Z2.65, absorptionZ1.0%, and field moistureZ3.9%. The fly ash used had a specific gravity of 2.50. The

superplasticizer was naphthalene based one with a specific

gravity of 1.21 and a solid content of 42%. What is the

composition of 1 m3 of such concrete?

Appendix D. Data for batch composition

A concrete batch plant is to produce 8 m3 of HPC on the

basis of the following SSD composition [15]: w/cZ0.29,

waterZ130 l, cementZ450 kg, coarse aggregatesZ1050 kg, fine aggregatesZ750 kg, and superplasticizerZ8 l (liquid) and 4 kg (solid). The aggregates having the

following water contents are in the bins: coarse aggregate-


moistureZ0.8%; and fine aggregate-specific gravity

(SSD)Z2.65, absorptionZ1.0%, and field moistureZ3.0%. The superplasticizer is a naphthalene superplasticizer

containing 42% solids and having a specific gravity of 1.21.

What are the masses of materials that must be weighed to

make 8 m3 of concrete?


References

[1] Neville AM. Properties of concrete, 4th ed. Essex: Longman Group

Limited; 1995.

[2] Akhras G, Foo HC. A knowledge-based system for selecting

proportions for normal concrete. Ex Sys Appl 1994;7(2):323–35.

[3] Bai Y, Amirkhanian SN. Knowledge-based expert system for concrete

mix design. J Cons Eng Mngt, ASCE 1994;120(2):357–73.

[4] Islam MN, Al-Mattarneh HMA, Zain MFM, Basri HB. Towards an

expert system for HPC mix design. Proceedings of the world

conference on concrete materials and structures, Shah Alam,

Malaysia, May; 2002.

[5] Foo HC, Akhras G. Expert systems and design of concrete mixtures.

Conc Int 1993;15(7):42–6.

[6] Oh J, Lee I, Kim J, Lee G. Application of neural networks for

proportioning of concrete mixes. ACI Mat J 1999;96(1):61–7.

[7] Durkin J. Expert systems design and development. New Jersey:

Macmillan; 1994.

[8] Hickey JP, Aldridge AJ. An expert system for the prediction of

aquatic toxicity of contaminants. In: Hushon JM, editor. Expert

systems for environmental applications. Washington: American

Chemical Society; 1990.

[9] Smith LM. Interim report on COMIX: an expert system for concrete

mix design, Report No. M4.87/1, Central Laboratories, New Zealand;

1987.

[10] Malasri S, Maldonado S. Concrete mix designer. Comp Appl Conc

Tech, ACI 1988;SP113-3:33–41.

[11] Islam MN, Al-Mattarneh HMA, Zain MFM, Basri HB. Expert

systems and concrete mix design: a review. Proceedings of the sixth

international conference on concrete technology for developing

countries, Amman, Jordan, October; 2002.

[12] Malier Y, editor. High performance concrete: from material to

structure. London: E and FN Spon; 1992.

[13] Shah SP, Ahmad SH, editors. High performance concrete and

applications. London: Edward Arnold; 1994.

[14] Nawy EG. Fundamentals of high strength high performance concrete.

London: Longman Group Limited; 1996.

[15] Aitcin PC. High-performance concrete. London: E and FN Spon;

1998.

[16] Safiuddin M. Influence of different curing methods on the mechanical

properties and durability of HPC exposed to medium temperature.

MSc Thesis, Universiti Kebangsaan Malaysia, 1998.

[17] Day KW. Concrete mix design, quality control and specification,

second ed. London: E and FN SPON; 1999.

[18] ACI Committee 211. Standard practice for selecting proportions for

normal, heavyweight, and mass concrete (ACI 211.1-91). Detroit:

American Concrete Institute; 1991.

[19] ACI Committee 363. State-of-the-art report on high-strength concrete

(ACI 363R-92). Detroit: American Concrete Institute; 1992.

[20] ACI Committee 211. Guide for selecting proportions for high strength

concrete with Portland cement and fly ash (ACI 211.4R-93). Detroit:

American Concrete Institute; 1993.

[21] ACI Committee 201. Guide to durable concrete. ACI Manual of

Concrete Practice: Part 1 (ACI 201.2R-92). Detroit: American

Concrete Institute; 1995.

[22] de Larrard F. A method for proportioning high-strength concrete

mixtures. Cem Conc Agg 1990;12(2):47–52.

[23] Mehta PK, Aitcin PC. Principles underlying production of high-

performance concrete. Cem Conc Agg 1990;12(2):70–8.

[24] Rougeron P, Aitcin PC. Optimisation of the composition of high-

performance concrete. Cem Conc Agg 1994;16(2):115–24.

[25] Mehta PK, Aitcin PC. Microstructural basis of selection of

materials and mix proportions for high-strength concrete. Proceed-

ings of the one-day short course on concrete technology/HPC:

Properties and Durability, University Malaya, Kuala Lumpur,

May; 1997.

[26] Aitcin PC. Durable concrete: current practice and future trends.

Proceedings of the one-day short course on concrete technology/HPC:

Properties and Durability, University Malaya, Kuala Lumpur, May;

1997.

[27] Aitcin PC. The durability of high performance concrete. Proceedings

of the one-day short course on concrete technology/HPC: Properties

and Durability, University Malaya, Kuala Lumpur, May; 1997.

[28] Mahmud H. Cement replacement materials in concrete- for strength,

durability and economy. Proceedings of the one-day short course on

concrete technology/HPC: Properties and Durability, University

Malaya, Kuala Lumpur, May; 1997.

[29] Neville A, Aitcin PC. High-performance concrete—an overview.

Materials and Structures 1998;31:111–7.

[30] Islam MN, Zain MFM, Basri HB. Development of an expert system

for high performance concrete mix design. Proceedings of the

international conference on robotics, vision, information and signal

processing, Penang, Malaysia, January, 2003.

[31] IntelliCorp Inc. KAPPA-PC Ver. 2.4 User’s Manual, IntelliCorp Inc.,

CA, 1997.

[32] Basri HB. An expert system for preliminary landfill design in

developing countries. PhD Thesis, Department of Civil Engineering,

University of Leeds; 1994.

[33] Basri HB. An expert system for landfill leachate management. Env

Tech 2000;21:157–66.

[34] Basri NEA. An expert system for the design of composting facilities in

developing countries. PhD Thesis, Department of Civil Engineering,

University of Leeds, 1999.

[35] Kaetzel LJ, Clifton JR, Klieger P, Snyder K. Highway concrete

(HWYCON) expert system user reference and enhancement guide.

Gaithersburg: National Institute of Standards and Technology

(BFRL); 1993.

Documents

2005