The document discusses the evolution of paradigms for designing civil engineering systems. It identifies five classical paradigms (A through E) and proposes a new emerging paradigm (F):
1. The empirical model (Paradigm A), static descriptive deterministic method (Paradigm B), static stochastic descriptive model (Paradigm C), and dynamic stochastic descriptive model (Paradigm D) describe previous approaches.
2. The new proposed paradigm is the interactive multi-attribute learning paradigm (Paradigm F), which involves identifying actors, modeling structures, and using tools to generate alternatives, simulate responses, communicate features, and assess reactions and preferences in an interactive learning process.
Damage detection in cfrp plates by means of numerical modeling of lamb waves ...eSAT Journals
Abstract
The paper presents an application of modeling acoustic waves propagation in a carbon fiber reinforced plastic (CFRP) plates for
damage detection. This task is a part of non-destructive testing (NDT) methods which are very important in many industry
branches. Propagation of Lamb waves is modeled using three-dimensional finite element method by means of commercial
software. In the paper three different cases of plate structures with and without flaws are considered to present review of selected
methods for the detection of defects in time and frequency domain. These are comparisons of: A-scans, B-scans, dispersion
curves, spectrograms, scalograms and energy plots. Developed numerical model first has been validated by means of analytical
solution for isotropic plate.
Keywords: Lamb waves, non-destructive testing, finite element method, damage detection
Dual-time Modeling and Forecasting in Consumer Banking (2016)Aijun Zhang
Longitudinal and survival data are naturally observed with multiple origination dates. They form a dual-time data structure with horizontal axis representing the calendar time and the vertical axis representing the lifetime. In this talk we discuss how to model dual-time data based on a decomposition strategy and how to forecast over the time horizon. Various statistical techniques are used for treating fixed and random effects.
Among other fields, we share the potential applications in quantitative risk management, and demonstrate a large-scale credit risk analysis powered by big data computing.
Damage detection in cfrp plates by means of numerical modeling of lamb waves ...eSAT Journals
Abstract
The paper presents an application of modeling acoustic waves propagation in a carbon fiber reinforced plastic (CFRP) plates for
damage detection. This task is a part of non-destructive testing (NDT) methods which are very important in many industry
branches. Propagation of Lamb waves is modeled using three-dimensional finite element method by means of commercial
software. In the paper three different cases of plate structures with and without flaws are considered to present review of selected
methods for the detection of defects in time and frequency domain. These are comparisons of: A-scans, B-scans, dispersion
curves, spectrograms, scalograms and energy plots. Developed numerical model first has been validated by means of analytical
solution for isotropic plate.
Keywords: Lamb waves, non-destructive testing, finite element method, damage detection
Dual-time Modeling and Forecasting in Consumer Banking (2016)Aijun Zhang
Longitudinal and survival data are naturally observed with multiple origination dates. They form a dual-time data structure with horizontal axis representing the calendar time and the vertical axis representing the lifetime. In this talk we discuss how to model dual-time data based on a decomposition strategy and how to forecast over the time horizon. Various statistical techniques are used for treating fixed and random effects.
Among other fields, we share the potential applications in quantitative risk management, and demonstrate a large-scale credit risk analysis powered by big data computing.
FRACTIONAL CALCULUS APPLIED IN SOLVING INSTABILITY PHENOMENON IN FLUID DYNAMICSIAEME Publication
The purpose of present paper is to find applications of Fractional Calculus approach in Fluid Mechanics. In this paper by generalizing the instability phenomenon in fluid flow through porous media with mean capillary pressure with transforming the problem into Fractional partial differential equation and solving it by applying Fractional Calculus and special functions.
Our report deals with Growth curves, perhaps one of the most quantitative way to forecast a technology. We tried to present growth curves in a nutshell, encompassing different types of it, from symmetric to non-symmetric growth curves.
Numerical simulations have been undertaken
for the benchmark problem in a Square cavity by using
computational fluid dynamics software. This work aims at
discussing the fundamental numerical and computational
fluid dynamic aspects which can lead to the definition of
the following meshing methods and turbulence models.
The meshes used for the simulation are hexahedral,
hexahedral cell with near wall refinement, tetrahedral
grid, polyhedral, tetrahedral with near wall refinement
and polyhedral mesh with prism layer cells based the near
wall thickness of Y+ less than one. The turbulence models
used for the simulation work are AKN K-Epsilon Low-Re,
Realizable K-Epsilon, Realizable K-Epsilon Two-Layer,
standard K-Epsilon, standard K-Epsilon Low-Re,
Standard K-Epsilon Two-Layer, V2F K-Epsilon,
SST(Menter) K-Omega, and Standard(Wilcox) K-Omega.
From these meshes and turbulence models, we will
conclude the suitable mesh and turbulence for the
recirculation flow by the grid independent test. These
analytical values of results are compared with reference
data which gives an optimization of experimental work.
Unsteady simulation was ran for all the Grid Independent
mesh with the SST k omega model with the time step of
0.01 sec for 40 seconds. The flow nature is studied with
and without the temperature for Reynolds number, 1000
and 10000.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Large Eddy Simulation of Turbulence Modeling for wind Flow past Wall Mounted ...IJERA Editor
This paper will present the large eddy simulation of turbulence modeling for wind flow over a wall mounted 3D cubical model. The LES Smagorinsky scheme is employed for the numerical simulation. The domain for this study is of the size of 60 cm x 30 cm x 30 cm. The 3D cube model is taken of the size of 6 cm x 6 cm x 4 cm. The Reynolds number for the flow in respect of the height of the cube i.e, 4 cm is 5.3x104. The hexahedral grids are used for the meshing of the flow domain. The results are discussed in terms of various parameters such as velocity profile around the cube and the computational domain, the pressure distribution over the cube, near wall velocity profile and the shear stress distribution and also the result of drag coefficient is verified by neural network time series analysis using MATLAB. In this present study we have used the OpenFoam platform for the computational and numerical analysis. The numerical scheme employed is the combination of the steady state incompressible Newtonian flow model using SIMPLE algorithm followed by the transient model of incompressible Newtonian flow using PISO algorithm. We have observed that there is a constant positive drag coefficient in case of steady state simulation where as there is a negative lift coefficient in the initial run and a very low lift coefficient at the end of the steady state simulation.
Multi-Hazard Assessment of Bridges in Case of Hazard Chain: State of Play and...Franco Bontempi
This study focuses on multi-hazard analysis for bridges, following a two-tier approach.
First, it identifies relevant open issues and recent literature developments in the field, presenting data in a meaningful manner, with specific focus on the issues related with the analysis of hazard chain scenario treated as low probability–high consequence events.
Second, it describes a practically useful and sufficiently generic approach for efficient computational investigation of hazard chain scenarios in highway bridges.
Following that, the applicability of the approach is exemplified in an appealing and commonly encountered in real-life hazard chain scenario, in which a multilevel modeling strategy is adopted to assess the structural response under hazard chain scenarios of a highway viaduct. Among the considered scenarios is the impact of a heavy vehicle (tank truck) on the bridge pier, and the fire spread following the collision due to the
presence of inflammable materials. The bridge structure is a typical 189-m-long multispan composite highway viaduct. The impact is modeled with a non-linear transient dynamic analysis that accounts the inertial effect of the global structure, while the
fire modeling is performed with non-linear quasi static dynamic analysis focusing on local behavior with a substructured model. Then different impact and fire scenarios are considered, including different impact velocities of the truck.
FRACTIONAL CALCULUS APPLIED IN SOLVING INSTABILITY PHENOMENON IN FLUID DYNAMICSIAEME Publication
The purpose of present paper is to find applications of Fractional Calculus approach in Fluid Mechanics. In this paper by generalizing the instability phenomenon in fluid flow through porous media with mean capillary pressure with transforming the problem into Fractional partial differential equation and solving it by applying Fractional Calculus and special functions.
Our report deals with Growth curves, perhaps one of the most quantitative way to forecast a technology. We tried to present growth curves in a nutshell, encompassing different types of it, from symmetric to non-symmetric growth curves.
Numerical simulations have been undertaken
for the benchmark problem in a Square cavity by using
computational fluid dynamics software. This work aims at
discussing the fundamental numerical and computational
fluid dynamic aspects which can lead to the definition of
the following meshing methods and turbulence models.
The meshes used for the simulation are hexahedral,
hexahedral cell with near wall refinement, tetrahedral
grid, polyhedral, tetrahedral with near wall refinement
and polyhedral mesh with prism layer cells based the near
wall thickness of Y+ less than one. The turbulence models
used for the simulation work are AKN K-Epsilon Low-Re,
Realizable K-Epsilon, Realizable K-Epsilon Two-Layer,
standard K-Epsilon, standard K-Epsilon Low-Re,
Standard K-Epsilon Two-Layer, V2F K-Epsilon,
SST(Menter) K-Omega, and Standard(Wilcox) K-Omega.
From these meshes and turbulence models, we will
conclude the suitable mesh and turbulence for the
recirculation flow by the grid independent test. These
analytical values of results are compared with reference
data which gives an optimization of experimental work.
Unsteady simulation was ran for all the Grid Independent
mesh with the SST k omega model with the time step of
0.01 sec for 40 seconds. The flow nature is studied with
and without the temperature for Reynolds number, 1000
and 10000.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Large Eddy Simulation of Turbulence Modeling for wind Flow past Wall Mounted ...IJERA Editor
This paper will present the large eddy simulation of turbulence modeling for wind flow over a wall mounted 3D cubical model. The LES Smagorinsky scheme is employed for the numerical simulation. The domain for this study is of the size of 60 cm x 30 cm x 30 cm. The 3D cube model is taken of the size of 6 cm x 6 cm x 4 cm. The Reynolds number for the flow in respect of the height of the cube i.e, 4 cm is 5.3x104. The hexahedral grids are used for the meshing of the flow domain. The results are discussed in terms of various parameters such as velocity profile around the cube and the computational domain, the pressure distribution over the cube, near wall velocity profile and the shear stress distribution and also the result of drag coefficient is verified by neural network time series analysis using MATLAB. In this present study we have used the OpenFoam platform for the computational and numerical analysis. The numerical scheme employed is the combination of the steady state incompressible Newtonian flow model using SIMPLE algorithm followed by the transient model of incompressible Newtonian flow using PISO algorithm. We have observed that there is a constant positive drag coefficient in case of steady state simulation where as there is a negative lift coefficient in the initial run and a very low lift coefficient at the end of the steady state simulation.
Multi-Hazard Assessment of Bridges in Case of Hazard Chain: State of Play and...Franco Bontempi
This study focuses on multi-hazard analysis for bridges, following a two-tier approach.
First, it identifies relevant open issues and recent literature developments in the field, presenting data in a meaningful manner, with specific focus on the issues related with the analysis of hazard chain scenario treated as low probability–high consequence events.
Second, it describes a practically useful and sufficiently generic approach for efficient computational investigation of hazard chain scenarios in highway bridges.
Following that, the applicability of the approach is exemplified in an appealing and commonly encountered in real-life hazard chain scenario, in which a multilevel modeling strategy is adopted to assess the structural response under hazard chain scenarios of a highway viaduct. Among the considered scenarios is the impact of a heavy vehicle (tank truck) on the bridge pier, and the fire spread following the collision due to the
presence of inflammable materials. The bridge structure is a typical 189-m-long multispan composite highway viaduct. The impact is modeled with a non-linear transient dynamic analysis that accounts the inertial effect of the global structure, while the
fire modeling is performed with non-linear quasi static dynamic analysis focusing on local behavior with a substructured model. Then different impact and fire scenarios are considered, including different impact velocities of the truck.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
A Review Of Major Paradigms And Models For The Design Of Civil Engineering Systems
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
aliates 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 sucient
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 eciency 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 ecient
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 (Grin 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 coecients (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 dicult 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
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 coecients 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 oce
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 oces 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''.
References
Accreditation Board for Engineering and Technology, 1991.
Criteria for accrediting programs in engineering in the
United States. Accreditation Board for Engineering and
Technology, New York.
Ang, A.H.S., 1972. Structural Safety, A Literature Review.
ASCE, 98, ST4, 845.
Ang, A.H.S., Tang, W.M., 1975. Probability Concepts in
Engineering Planning and Design, vol. 1, Basic Principles.
Wiley, New York.
Association Qualitel, 1980. Guide Qualitel. Association Quali-
tel, Paris.
Belck, R.W., 1988. Possessions and the extended self. Journal of
Consumer Research 14, 136±168.
Beech, L.R., 1990. Image Theory: Decision Making in Personal
and Organizational Contexts. Wiley, New York.
Butler, D., Richmond, D., 1990. Advanced Valuations. Mac-
Millan, New York.
Ellul, J., 1964. The Technological Society. Knopf, New York.
Feldman, J., Lindell, M.K., 1989. On Rationality in Horowits,
I. Organisation and Decision Theory. Kluwer Academic
Publishers, Dordrecht.
12 L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13
13. Ferry-Borges, J., Castanheta, M., 1971. Structural safety.
LNEC Portugal.
Flormann, 1994. The Existential Pleasures of Engineering. St.
Martin's Press, New York.
Grin, Ronald, C., 1998. The fundamental principles of cost
bene®t analysis. Walter Resources Research 34 (8), 2063±
2071.
Levy, S., 1959. Symbols for sale. Harvard Business Review 37
(4), 117±124.
Mass, A., Hufschmidt, M.M., Dorfman, R., Thomas, H.A.,
Marglin, S.A., Fair, G.M., 1962. Design of Water-Resource
Systems. Harvard University Press, London.
Meyer, R., Johnson, E.J., 1995. Empirical generalizations in the
modelling of consumer choice. Marketing Science 14 (3),
G180±189.
Morris, C.D., Laboube, R.A., 1995. Teaching civil engineering
design: Observations and experiences. Journal of Profes-
sional Issues in Engineering Education and Practice 47±53.
Mumford, L., 1967. The Myth of the Machine: Technics and
Human Development. Harcourt, Brace and World, New
York.
Mumpower, J.L., Rorbaugh, J., 1996. Negotiation and design:
Supporting resource allocation decisions through analytical
mediation. In: Melvin, Shakun, F. (Eds.), Negotiation
Processes: Modeling Frameworks and Information Tech-
nology. Kluwer Academic Publishers, Boston, MA.
Nigam, N.C., 1986. Optimum design of systems operating in
random vibrations environment. In: Elishako€, Lyon
(Eds.), Random Vibration. Elsevier, Amsterdam.
Rao, S.S., 1978. Optimization Methods in Engineering. Wiley,
New York.
Rau, J.G., 1970. Optimisation and Probability in Systems
Engineering. Van Nostrand Reinhold, New York.
Reich, C., 1970. The Greening of America. Random House,
New York.
Reynolds, T.S., 1991. The Engineer in America. Chicago Press.
Roy, B., 1985. M
ethodologie Multicritere d'aide
a la Decision.
Economica, Paris.
Simon, M., 1969. The Sciences of the Arti®cial. MIT Press,
Cambridge, MA.
Simonson, I., Tuersky, A., 1992. Choice in context: Trade-o€
contrast and extremeness. Journal of Marketing Research
29, 195±231.
Stevens, J.D., Glagola, C., Ladbetter, W.B., 1994. Quality-
measurement matrix. Journal of Management in Engineer-
ing 30±35.
Tavares, L.V., 1977. Extremes of autocorrelated load model.
ASCE Journal of the Engineering Mechanics Division, Aug.
717±724.
Tavares, L.V., 1984. The TRIDENT approach to rank alter-
natives tenders for large engineering projects. Foundation of
Control Engineering 9 (4), 181±193.
Tavares, L.V., 1988. Generalized transitivity and preferences
modelling: The concept of hyper-order. European Journal
of Operational Research 36, 14±26.
Tavares, L.V., 1998. Advanced Models for Project Manage-
ment. Kluwer Academic Publishers, Dordrecht.
Templeman, A., 1982. Civil Engineering Systems. MacMillan
Press, New York.
Tillman, F., Hwong, C., Kuo W., 1980. Optimisation of System
Reliability. Marcel Dekker, New York.
Torrance, E.P., 1995. Why ¯y? A philosophy of creativity.
Ablex Publishing, Norwood, NJ.
Uhlik, F.T., Lores, G.V., 1998. Assessment of constructability
practices among general contractors. Journal of Architec-
tural Engineering 4 (3), 113±123.
Vries, M.J. de, Cross, N., Grant, D.P., 1993. Design Method-
ology and Relationship with Science. Kluwer Academic
Publications, Dordrecht.
Wiegand, J., Keller, T., 1980. Syst
eme d'
evaluation de loge-
ments, Berna.
L. Valadares Tavares / European Journal of Operational Research 119 (1999) 1±13 13