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1. Invited Review
A review of major paradigms and models for the design of civil
engineering systems
L. Valadares Tavares *
Department of Civil Engineering, Technical University of Lisbon, CESUR, Instituto Superior T
ecnico, DEC-IST, Av. Rovisco Pais,
1000 Lisbon, Portugal
Received 1 November 1998; accepted 1 November 1998
Abstract
In this paper, the author presents the ®ve classical paradigms of the process of design in civil engineering and
identi®es a new emerging paradigm: the interactive multi-attribute learning paradigm. This paradigm is studied in terms
of actors, structures and OR instruments which can help to ful®l its application to modern design of civil engineering
systems. Ó 1999 Elsevier Science B.V. All rights reserved.
Keywords: Design; Civil engineering system; Multi-attribute decision making; Paradigm
1. The evolution of the design of civil engineering
systems: Paradigms and challenges
Civil Engineering is devoted to the design and
construction of systems aiming to improve the con-
ditions of social, economic and environmental life.
It is one of the oldest types of engineering as it
was born to ful®l very basic needs of life such as
sheltering, transportation and river control. Some
of the most spectacular glories of the golden years
of technology at the beginning of this century were
achieved by civil engineers such as the railways
adventure, the heights of the new skyscrapers or
the irrigation of deserts by arti®cial lakes con-
tained by new types of dams (Reynolds, 1991).
Nowadays, it remains one of the most signi®-
cant branches of the profession of engineering as it
is shown by indicators such as the percentage of
aliates in engineering societies who are civil en-
gineers or the turnover of civil engineering ®rms
(Florman, 1994).
Creativity (Torrance, 1995) is the key ingredient
for any process of design and Civil Engineering is
no exception. However, the design of civil engi-
neering systems depends also heavily on the
adopted approach which had a very signi®cant
evolution not just described by the progress of the
available technology but also by the way how the
problem of design is formulated. The formulation
of this problem depends on the data and on the
scienti®c results which can be used, (see Vries et al.,
1993) and it describes the speci®c civil engineering
culture of each stage of this process of evolution.
European Journal of Operational Research 119 (1999) 1±13
www.elsevier.com/locate/orms
*
Tel.: +351 1 8418 310; fax: +351 1 8409 884; e-mail:
cesur@civil8-ist.utl.pt
0377-2217/99/$ ± see front matter Ó 1999 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 7 - 2 2 1 7 ( 9 8 ) 0 0 3 6 2 - 2

2. The study of such process can be done by the
analysis of the paradigm adopted in each stage to
formulate and solve the process of design (see
Simon, 1969).
The evolution of this paradigm is closely con-
nected to the type of model related to formulate the
problem of design and most of the recent devel-
opments in modelling are based and OR method-
ology and tools.
Until the forties, a lot of expertise to design civil
engineering was accumulated and presented
through empirical relations connecting:
(a) natural exogenous conditions (G) with the
corresponding design loads (L), and
(b) the design loads (L) with the design variables
(X) to be adopted for the civil engineering sys-
tem.
Common examples are:
· G ˆ catchment area of a river basin; L ˆ design
storm; X ˆ reservoir capacity.
· G ˆ expected population using a parking lot;
L ˆ required capacity; X ˆ parking area and size
of access lanes.
In this stage, the activity of design is basically
carried out using models with an empirical nature:
Paradigm A ± The empirical model:
L ˆ f G†;
X ˆ g L†:
The important development of analytical
methods since the forties has enabled the substi-
tution of the empirical relationship g(L) by a static
deterministic description of the civil engineering
system relating the design loads (L) with the re-
sponse variables (R). This static model is usually
based on energy equilibrium conditions (e.g., for
structural design), on inventory models (e.g., for
the design of reservoirs) or on mass conservation
laws (e.g., for the design of water supply net-
works). Thus, the previous paradigmatic model
was substituted by the following paradigm:
Paradigm B ± The static descriptive deterministic
method
L ˆ f G†;
R ˆ q0
L; X0
†;
X00
ˆ q00
R†;
where q0
is the static behaviour function and X 0
is
a set of design variables. The design is complete by
setting up another set of design variables, X 00
, in
terms of R, X00
ˆ q00
(R), in order that the system
will cope adequately with the response variables
(see Templeman, 1982).
This type of model can be illustrated by the
example of designing a structure in terms of the
speci®ed load (L) where X0
is the set of parameters
de®ning the structure (geometry, weight, etc.), R is
the set of the generated stresses at key sections of
the structure and X00
includes the design parame-
ters (type of materials, etc.) achieving sucient
resistance to cope with such stresses.
This approach implies a good deal of inspira-
tion and of experience to set up good X00
solutions
and the full understanding of the response func-
tions.
The rapid development of science and technol-
ogy after the second world war has provided Civil
Engineering with more e€ective models to describe
the uncertainty and the stochastic nature of the
system's loads and with more accurate models to
describe the physical behaviour of the designed
systems.
The former results have allowed the develop-
ment of advanced risk analyses (Ang and Tang,
1975) and the latter ones have suggested a wide
spectrum of new technological solutions.
This means that (L) is then substituted by a
stochastic process (see, e.g., Tavares, 1977) of the
occurring loads
L ˆ fLtg and that the static de-
terministic descriptive model can be substituted by
the following static stochastic descriptive model:
Paradigm C ± The static stochastic descriptive
model:
L ˆ fLt=Gg;
R ˆ q0
L; X0
†;
X00
ˆ q00
R†;
S R; X00
† P a;
where S R; X00
† P a is a safety condition being
S R; X00
† the probability of non-failure and a the
minimal safety threshold (Ang, 1972; Mass et al.,
1962; Ferry-Borges and Castanheta, 1971).
The formulation of the system's behaviour in
terms of the probabilistic de®nition of its safety is
2 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13

3. the major advantage of this model over B. More
recently, a further model has been proposed con-
sidering the dynamic behaviour of the system, the
dynamic stochastic descriptive model:
Paradigm D ± The dynamic stochastic descrip-
tive model:
L ˆ fLt=Gg;
R ˆ q0
L; X0
; t† for 0 6 t 6 T;
X00
ˆ q00
R†;
skfR; X00
g 6 ak for k ˆ 1; . . . ; k;
where k ˆ 1; . . . ; K corresponds to each mode of
safety to be considered.
This model has been successfully applied to a
wide range of systems under loads generated by
turbulence processes, strong winds, ocean waves,
earthquakes, track unevenness, etc. Usually, the
safety restrictions include a ®rst set of conditions
on the moments and a second set on the maximal
and minimal eigenvalues (Nigam, 1986).
The models C and D require the collection and
treatment of much larger volumes of numerical
data conveniently distributed on time and on
space. This has become feasible due to the expo-
nential advances of the data and computer systems.
The study of R is usually carried out by the sim-
ulation methodology using extensive numerical
experimentation. This formulation can be trans-
formed into the classical problem of the Optimi-
sation Theory where the objective will be expressed
by a scalar objective function, F, and by a set of
restrictions (Rao, 1978; Rau, 1970; Tillman et al.,
1980) producing a new paradigm:
Paradigm E ± The single criterion optimisation:
max
min
u ˆ F L; X†
h1
L; X† P 0;
h2
L; X† P 0;
h3
L; X† P 0;
h4
L; X† P 0;
h5
L; X† P 0;
with u a scalar function such as the cost to be
minimized or the stability to be maximized,
L the
uncontrolled variables and X the decision vari-
ables, h1 a ®rst set of restrictions describing the
physical behaviour of the system, h2 a second set of
restrictions de®ning the feasible domains of the
decision variables, h3 a third set of restrictions
describing legal and normative conditions, h4 a
fourth set of restrictions describing constraints on
other attributes also important to assess the im-
pact and quality of the achieved design, and h5 a
®fth set of restrictions concerning the safety
probabilistic conditions.
The theory of optimisation has been intensively
used to ®nd out the design solution achieving the
maximal or minimal value for u.
The modelling of L, the use of simulation
methods to study R and the optimisation of F are
three major contributions of Operational Research
to the theory and the practice of the design of civil
engineering systems (models B, C, D and E).
During the last decades, criticism about less
successful civil engineering projects have grown up
all over the world and the need to avoid the per-
verse e€ects of technology has become a dominant
feature of our modern culture (see, e.g., Ellul,
1964; Mumford, 1967; Reich, 1970). Therefore,
new challenges are demanding for more harmonic,
comprehensive and interactive models of the pro-
cess of design in civil engineering:
· the need to give additional attention to the pres-
ervation of environmental conditions;
· the need to design systems better integrated
into the social, economic and political environ-
ments;
· the need to pro®t from new technologies to en-
hance the performance of the designed systems;
· the need to guarantee higher levels of quality
and of its perception by the users;
· the need to achieve higher levels of economy,
particularly in terms of scarce natural resources
such as land, space, water or energy;
· the need to combine the physical design with the
®nancial and commercial design in order that
the feasibility, the eciency and the pro®tability
of the obtained solutions will be maximised;
· the need to consider in the process of design a
wider range of actors and stakeholders through
more or less structured systems (public audits,
groups of advisors, etc.).
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 3

4. Answering to these needs implies a signi®cant ef-
fort to develop a new paradigm which should be
based on three major principles:
· the design is a complex decision process involv-
ing the interaction of multiple actors due to the
multi-sectoral e€ects of the selection of any de-
sign;
· most of these actors can have di€erent values,
objectives and preferences due to their di€erent
nature;
· the search for a better design implies a learning
process based on multiple comparisons of di€er-
ent alternatives due to the complexity and mul-
tiplicity of relevant perspectives
Thus, the new emerging paradigm can be called
the interactive multi-attribute learning paradigm:
Paradigm F ± The interactive multi-attribute
learning paradigm. The major stages of the process
of design according to this paradigm are:
This approach is much more ``context-orient-
ed'' than the previous paradigmatic models and it
has a cyclic nature as the assessment of the gen-
erated alternatives suggests their improvement.
The applicability of this model requires ecient
and e€ective systems to generate feasible alterna-
tives, to simulate their response, to communicate
their features to the major actors and to assess
their reactions and their preferences.
The adoption of this approach implies also
signi®cant changes in the way civil engineering
design should be taught (Morris and Laboube,
1995).
The recent OR developments provide these in-
struments for most areas of Civil Engineering.
Unfortunately, they are often used to implement
older paradigms rather than to create a new ap-
proach to design civil engineering systems. An il-
lustration of this paradox is the use of visual
computing outputs which just perform the role
played by traditional print-outs rather than es-
tablishing interactive and learning processes with
the actors concerned with the design. The appli-
cation of this new paradigm implies a more de-
tailed analysis of its three major elements:
· identi®cation of actors,
· modelling of structures,
· analysis of instruments.
This is the object of Section 2 where the role of OR
is discussed too.
2. The interactive multi-attribute learning paradigm
for civil engineering design
2.1. The actors
The process of designing a system in civil en-
gineering always should be oriented to ful®l ex-
isting (or forecasted) needs (Feldman and Lindell,
1989). However, the process of understanding,
analysing and modelling such needs is complex not
just because they have multiple attributes but also
because there are di€erent actors involved in the
process of identifying their features:
Bene®ciaries: Those whose needs are supposed
to be ful®lled by the system. Traditionally, these
bene®ciaries are called ``consumers'' as it happens
with the system like a public water supply network
or a commercial centre. However, the concept of
bene®ciary is more accurate because in some cases
there is no e€ective consumption of any good or
service as it may happen if the system is a natural
park.
Users: Those who will participate in the oper-
ation of the system. In most cases, the users are the
same as the bene®ciaries as it is the case of a road,
of a private house or of a commercial area but
many examples of side e€ects illustrate the alter-
native case such as:
(a) the owner of a land which has a much higher
value due to the nearby construction of a high-
way is a bene®ciary but not a user;
4 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13

5. (b) the user of a medical centre which is much
better o€ after a new hospital is opened because
it has decongested that centre is a bene®ciary
but not an user of such a hospital.
The bene®ciaries who do not use the system may
be called indirect bene®ciaries in opposite to the
other ones, direct bene®ciaries.
Pressure groups: The public or private interest is
often supported by pressure groups also active in
the process of decision making about a new engi-
neering system. Nowadays, there is a wide spec-
trum of these groups covering private rights (e.g.,
landowners) or addressing public issues like the
environment.
Promoters: Those who are in charge of the
system's development. They play a crucial role in
de®ning the objectives to be achieved by the design
and, in general, they are the ``client'' of the de-
signer. They also carry out the commercialisation
of the system or they sub-contract such roles to
other institution.
Owners: Those who will possess the system.
Traditionally, they were also the promoters but the
general trend is the opposite one.
Project manager: The person or the institution in
charge of the management of the whole process
fromthebeginningoftheconceptionofthedesignto
the implementation and certi®cation of the system.
Builders: Those who will implement the sys-
tem's design.
Financial operators: Those who will provide the
®nancial resources.
Licensing authorities: Those who issue the re-
quired licenses and permit to build the system
Controllers and certi®ers: Those who will con-
trol, audit and certify the implementation of the
approved design.
The general network connecting those actors is
presented in Fig. 1. This means that the process of
identifying the needs to be ful®lled by the designer
and the requirements to be considered is a multi-
actor and dynamic process with multiple levels of
Fig. 1. The network connecting the major actors of the process of design in Civil Engineering.
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 5

6. analysis and of interaction. A poor expression of
such needs leaves the designer abandoned to his
own preferences and then the ®nal degree of sat-
isfaction tends to be higher for himself than for the
client of the designed systems. Therefore, the
process of design should be supported by appro-
priate representations of the reality (descriptive
models) as well as by systems supporting the
communication, the evaluation, the negotiation,
the upgrading of alternatives until converging to
the ®nal decision.
2.2. The structure
The process of design in civil engineering can
now be substantially improved by the development
of a multi-attribute structure to compare alterna-
tive solutions, to suggest new ones and to support
the process of selection of the most convenient
design (highest total quality).
Such a structure is also an important instru-
ment for communication between the multiple
actors already introduced and it will minimise the
risk of ignoring important unplanned e€ects which
may undermine the utility of the new system.
The proposed framework follows a tree-struc-
ture as is presented in Fig. 2. Tree-structures have
been intensively used in many areas of OR and
hence its contribution can be quite substantial.
The top node of this value tree corresponds to the
total quality of each design alternative which is
branched into three major attributes concerning:
· the process,
· the system,
· the context.
The perspectives most directly relevant to the ac-
tors in charge of the legal, the ®nancial and the
construction decisions are included within the ®rst
branch.
The legal aspects depend strongly on the
country where the system is to be built. Financial
assessment can be much improved by scenario or
simulation models and the study of constructabi-
lity or control is becoming a key area in Civil
Engineering (see, Uhlik and Lores, 1998).
Fig. 2. The proposed value tree.
6 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13

7. The second branch concerns the system itself
and its evaluation stems from three major attri-
butes (aesthetics, functionality, economy) which
can be branched down into groups of three other
more speci®c aspects.
The branch concerning ``ful®lment of needs''
within ``functionality'' can be particularly impor-
tant as it can cover major functional bene®ts to
be taken into account and not considered by ®-
nancial analysis. Recent recommendations about
the use of cost±bene®t analysis are helping to
adopt stable procedures with the purpose of es-
timating the relevant bene®ts (Grin and Ro-
nald, 1998).
The third branch covers the attributes describ-
ing the integration of the system into the envi-
ronmental, social, political and cultural spaces.
The comparative analysis of di€erent designs
implies the application of each of these attributes
to each alternative solution and the construction
of decision matrices following this structure.
The construction of such matrices can improve
the process of de®ning the overall quality (Stevens
et al., 1994), can support the appropriate bench-
marking and can also generate additional and
more convenient solutions.
The construction of this matrix implies the
operationalization of the assessment of each al-
ternative in terms of each attribute which re-
quires the de®nition of an appropriate indicator
with a quantitative or qualitative nature (e.g.,
bad, reasonable, good or very good quality). In
several cases, such assessment can be produced
by the average opinion of a group of experts who
express their opinion along an arbitrary scale
(e.g., 0 ® 10). Probabilistic scales can be also
very useful to assess some features (e.g., the risk
of failure). General suggestions for the assess-
ment in terms of each attribute are presented in
Table 1.
2.3. The instruments
Multiple types of instruments can be applied to
the presented structure based on Multicriteria
Decision Theory and on Negotiation Theory.
Several analyses are particularly important.
2.3.1. Checking the consistency of the decision
matrix data
The assessment of each alternative is not an
easy task and therefore procedures should be im-
plemented to reveal any inconsistency and to cor-
rect it. Models based on Relational Systems of
Preference such as the Pre-Order, Quasi-Order
(Roy, 1985) or Hyper-Order (Tavares, 1988) can
be successfully used.
2.3.2. Elimination of unsatisfactory alternatives
Usually, minimal levels of satisfaction are set
up for each attribute and hence a preliminary
screening can eliminate any alternative not com-
plying with one or more of these levels.
2.3.3. Synthetic assessment of alternatives
A long list of models have been proposed to aid
the process of multi-attribute comparison of dis-
crete alternatives but many of them are hardly
applicable to the studied problem. The most tra-
ditional approach is based on the synthetic as-
sessment of each alternative through a weighted
average of the assessment in terms of each attrib-
ute (compensatory approach). This is the most
common model implying:
(a) a metric scale for the scalar assessing each al-
ternative, i, in terms of each attribute, j, uij.
(b) the transformation of each metric scale into
a value function.
Several models can be used to build such a func-
tion, v, using a constant and linear relation: vij ˆ
uij ÿ mij†= Mj ÿ mj† if a higher uij is preferable to
a lower one or vij ˆ Mj ÿ uij†= Mj ÿ mj† other-
wise, Mj and mj being the maximal and the mini-
mal bounds of uij.
These limits can be drawn up from the set of
alternatives or can be reasonable extremes for the
acceptable domain of uij. This last option avoids
the unstability which may occur with the former if
changes in the set of alternatives are introduced
during the process of analysis.
More sophisticated models can help the actors
to construct the value function, point by point, as
it is in the case of Macmodel (Tavares, 1998) (see
Fig. 3).
(c) the construction of a weighted average,
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 7

8. vi ˆ
X
N
jˆ1
kj vij;
where kj are appropriate coecients (0 6 kj 6 1
with
P
jkj ˆ 1) expressing the relative importance
of each attribute j as one has
dvik
dvil
viˆcte
ˆ ÿ
kl
kk
;
which represents the trade-o€ for any pair of at-
tributes (k, l).
Obviously, this approach can be applied at
several levels of the tree and the total quality of
each alternative will be assessed by vi. The ranking
of alternatives should follow the decreasing or-
dering of this scalar.
This approach has implied the adoption of a
metric scale for the assessment of each attribute
which can be less obvious for more qualitative
aspects.
Also, two major drawbacks can be pointed out:
· the choice of kj
is dicult and controversial
Table 1
Assessment of attributes
Attribute Suggested indicators Scale
Legal compliance Probability of the design being approved 0±1 ( ± )
Duration of the process of approval 0, 1 (months)
Financial feasibility Risk of a ®nancial loss (R) ÿ1, +1
Expect net presented value (NPV) ÿ1, +1 (monetary units)
Internal rate of return (IRR) 0, +1 ( ± )
Constructability and control Degree of constructability (0±10) ( ± )
Cost of control (0, +8) (monetary units)
Aesthetics
Style, harmony, consistency Experts judgement (0±10) ( ± )
Functionality
Ful®llment of needs Degree of satisfaction of the users (0±10) or other bene®c scales ( ± )
Response to normal conditions Life span (0, +1) (years)
Response to extreme conditions Rate of degradation 0±100 (%)
Probability of failure under extreme
conditions (winds, seismic e€ects, etc.)
(0±1) ( ± )
Economy
Initial cost Investment (0, +1) (monetary units)
working cost Annual operational cost (0, +1) (monetary units/year)
Maintenance and repair cost Expected cost due to maintenance
during the life span
(0, +1) (monetary units)
Social and political integration Public satisfaction (0±10) ( ± )
Acceptance by central or local
administration
(0±10) ( ± )
Environmental equilibrium Environmental impact Speci®c indicator (BOD, etc. with speci®c scales)
Cultural heritage Risk of ecological disturbances (0±1) ( ± )
Disturbance or enhancing of cultural
values
(0±10) ( ± )
Risk of loss of cultural identity (0±1) ( ± )
8 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13

9. Fig.
3.
Examples
of
Macmodel
displays.
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 9

10. (see, e.g., Association Qualitel, 1980; Wiegand
and Keller, 1980);
· the synthesis producing vi is just a weighted av-
erage which may not represent conveniently the
decision maker. For instance, one may have
P
jkj vij
P
jkjvkj but if vkj vij for some j
,
then the decision maker may not accept that i
is better than k.
The former problem can be approached by sensi-
tivity analysis and a model was proposed with this
objective (Trident, Tavares, 1984). This model
produces the mapping of kj
for three dimen-
sions as it is required by the presented framework
(see example in Fig. 4).
The latter problem can be solved by adopting
an alternative approach to the synthetic assess-
ment of each alternative: pairwise comparisons.
This approach is developed in Section 2.3.4.
2.3.4. Pairwise comparison of alternatives
The study of pairwise comparisons between
alternatives can be carried out using the interesting
concept of ``outranking'' proposed by Roy (1985):
i outranks j iSj† if two conditions are fulfilled:
Concordance condition:
X
kj with vij
ÿ
h
P vkj
i X
kj
P A;
Fig. 4. Example of the Trident analysis for ®ve alternatives X1; . . . ; X5 (the indi€erence lines are denoted by Xi ˆ Xi0 ).
10 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13

11. Discordance condition:
max
j
vkj
ÿ
ÿ vij
6 B:
where A, B are thresholds to be de®ned. Obviously,
A has to be greater than or equal to 0.5. The dis-
cordance condition expresses the so-called ``Veto
condition'' which may be very signi®cant in many
instances. The outranking relation does not have
the same properties as the preference relation as it
is not transitive or even anti-symmetrical.
A graph of outranking relations can be pro-
duced considering that each alternative is repre-
sented by each mode and those alternatives not
being outranked by others (or if belonging to cir-
cuits whose nodes never receive an outranking
relation) will be the candidates to the ®nal selec-
tion (see example in Fig. 5).
An obvious extension of the presented relation
of outranking is specifying B in terms of j, Bj.
Another extension of this relation can be devel-
oped by substituting the concordance condition by
a weighted condition:
X
kj vij P
X
kj vkj
and keeping the discordance condition.
Several models ± Electre (Roy, 1985) ± were
developed following this approach.
2.3.5. Negotiation and upgrading of alternatives
A small set of alternatives can be pre-selected,
k 2 K, in terms of the previous analysis and then a
process of negotiation between the multiple actors
and of learning about how to improve the alter-
natives should take place.
A process of negotiation is a necessary condi-
tion to achieve acceptance by key actors and such
a process contributes also to a better modelling of
their value functions and of the coecients of
relative importance of the de®ned attributes. Sev-
eral models can be used to support this process of
negotiation (Mumpower and Rorbaugh, 1996).
This process implies learning to have a better
understanding about the strong and the weak as-
pects of each alternative and so multiple sugges-
tions tend to be produced to improve the K
alternatives.
Then, new alternatives can be generated from
each k, Gk ˆ k1; k2; . . .
f g with the purpose of up-
grading k. A more re®ned model can be used to
assess the achieved level of upgrading for each
generated alternative.
Actually, recent research (Simonson and
Tuersky, 1992; Meyer and Johnson, 1995) shows
that the assessment of the total quality by the
consumer can be particularly sensitive to the
strong advantage of one alternative, i, if compared
to another, k, in terms of one or a few speci®c
attributes which can have a symbolic value for the
consumer (e.g., the modernity, youngness, luxury,
etc.), S, and which de®ne the so-called motivational
®eld (Levy, 1959; Belck, 1988). Each dimension of
this ®eld correspond to one perceptual attribute
and to one-dimensional force driving the decision
maker to make his selection (Beech, 1990).
In Civil Engineering, many examples of this
problem occur in areas like the home and oce
markets or in the design of commercial and cul-
tural centres (Butler and Richmond, 1990). Evi-
dence has been collected showing that the
introduction of a speci®c feature in the design of a
private home like the existence of a whirlpool or of
an automated kitchen system may change sub-
stantially the comparison done by the clients. The
same applies to oces including less usual features
such as a hall with a decoration including dynamic
elements or showing a multi-screen projection.
The presented approaches (compensatory and
outranking models) are not particularly appro-
priate to cope with this situation and so an alter-
Fig. 5. Example of an outranking graph for six alternatives
fx1; . . . ; x6g producing a sub-set of candidates to the best al-
ternative ˆ fx1; x3; x5; x6g:
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 11

12. native model can be proposed: the symbolic model.
This model is based also on a pairwise approach
but adopts a new relation ± the advantage relation
± instead of the outranking relation. This relation
is de®ned by: i A k (i has an advantage relation
over k) if and only if:
max
j2S
vij ÿ vkj† P c
advantage condition; where S is sub-set of
symbolic attributes;
X
kj with vij
h
6 vkj†
i
=
X
kj
6 D
mass discordance condition; max vkj ÿ vij† E;
veto discordance condition;
where C is the advantage threshold, D the mass
discordance threshold and E the veto discordance
threshold. The ®rst condition is applied just to the
sub-set of symbolic attributes.
This relation expresses the relative advantage
accomplished by the alternative i over k despite a
weight mass favouring k because of the excep-
tional advantage of i over k in terms of, at least,
one attribute. The selection of C should be done
considering that C is the minimal di€erence pro-
ducing the advantage e€ect. It should be noted
that the veto threshold, E, expresses the maximal
di€erence in favour of the alternative k allowing a
favourable result to i and therefore it is reasonable
assuming that C should be greater than E. If so,
the following results can be proved (where i k
means that there is no advantage relation of i over
k and where ; means that there is no implication):
1. iAk ) k i (anti-symmetrical property),
2. iAm and mAk;iAk (no transitivity proper-
ty),
3. If [iAm and mAm0
and . . .m00
Aq] then it may
also happen qAi (circuit property).
3. Conclusions
The evolution of the process of designing civil
engineering systems was discussed and ®ve major
paradigmatic models were identi®ed:
· the empirical model,
· the static descriptive deterministic model,
· the static stochastic descriptive model,
· the dynamic stochastic descriptive model,
· the single criterium optimisation.
Recent developments and challenges suggest
another approach which was described by the in-
teractive multi-attribute learning paradigm.
This model is based on a more systematic ex-
ploration of the space of the alternative feasible
solutions and on their assessment in terms of
multiple attributes by the di€erent actors inter-
acting within the process of decision.
This approach helps to model the process of
design as a decision-making process following the
inspired de®nition of engineering design proposed
by the Accreditation Board for Engineering and
Technology (1991):
``Engineering design is the process of devising a
system, components or a process to meet desired
needs. It is a decision-making process (often iter-
ative), in which the basic sciences, mathematics,
and engineering sciences are applied to convert
resources optimally to meet a stated objective.
Among the fundamental elements of the design
process are the establishment of objectives, and
criteria, synthesis, constructions, testing and eval-
uations''.
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