Artificial Intelligence and Fuzzy Logic Applications in Construction

Artificial Intelligence
Emad Elbeltagi,
PhD, PEng
Mansoura University

Artificial Intelligence
An Overview and Its Applications in
Construction
Emad Elbeltagi,
PhD, PEng
Mansoura
University
21/09/2020 Emad Elbeltagi 2

What is AI?
• Artificial intelligence (AI), Machine
intelligence, Can machine think?
• Branch of computer science dealt with
building smart machines capable of
performing tasks that typically require
human intelligence
• Developing machines, computers, etc.
that think, behave, act and/or reason
rationally and humanly

AI vs. Human
Beings
• Human reasoning involves more than
just induction
• Computers never as good as humans
• In reasoning and making sense of
data
• In obtaining a holistic view of a
system
• Computers much better than humans
• In processing reams of data
• Performing complex calculations

Applied
Areas of AI• Robotics
• Artificial Neural Network
• Evolutionary Algorithms
• Decision Trees
• Data Mining
• Machine Learning

Applied
Areas of AI• Expert Systems
• Heuristic Search
• Computer Vision
• Fuzzy Logic
• Natural Language Processing
• Knowledge Representation
• Pattern Recognition
• Learning

Examples
• Driving on the highway
• Answering questions
• Recognizing speech
• Diagnosing diseases
• Translating languages
• Data mining

FUZZY LOGIC
Imprecise and Vague Information

Fuzzy Logic• Knowledge is important to solve difficult
problems, however is not always precise
• Uncertainties is always a characteristic
of knowledge
• Knowledge is used by human beings to
describe their thinking
Imprecise
Knowledge

Fuzzy Logic
• Natural language is vague and imprecise
• Healthy person
• Depressed patient
• Old/Young person
• Sweet Mango
• Beautiful Painting
Imprecise
Knowledge

Fuzzy Logic
• Single word can be used for different
meanings: “Long Period”
• Dinosaurs ruled the earth for a long
period (about millions of years)
• It has not rained for a long period
(about six months)
• I had to wait the doctor for a long
period (about four hours)
Imprecise
Knowledge

Fuzzy Logic
• Human being thinking, speaking, ..etc. is
fuzzy
• If an obstacle is close, then apply
brakes immediately
• It is hot and humid
• The vegetable is not properly cooked
• Cross the road whenever it is clear
Imprecise
Knowledge

Fuzzy Logic• In order to handle this imprecision in
knowledge, fuzzy logic has been
introduced
• Human do not feel comfortable with
precise speaking
• Describing the status of the sky
“Partially clouded sky”
• Or, one may say, “39.77 of the sky is
covered with clouds”
Imprecise
Knowledge

Black
or
White…
Does it belong to this color set?
Answer, YES or NO
Fuzzy→ No clear defined boundaries
Logic → makes sense
Fuzzy Logic

Black
or
White…
Answer, YES or NO
According to the great Greek scholar Aristotle
(384 BC – 322 BC ) who developed binary logic,
the answer is NO
Binary Logic:
YES or NO
TRUE or FALSE
ON or OFF
BELONGS TO or DOES NOT BELONG
1 or Ø
Ø Ø Ø Ø Ø … Ø
Fuzzy Logic

Black
or
White…
Answer, YES or NO
In 1964, Lotfi A. Zadeh (1921) presented what is
called Fuzzy Sets & Fuzzy Logic
Ø Ø Ø Ø Ø … Ø
Simply a scale between 0
and 1 to present the degree
of belonging or membership
Binary
Fuzzy0.8 Ø 0.3 Ø Ø … Ø
Fuzzy Logic

“You do not need to be that precise to
solve a problem or reach a conclusion”,
this is a kind of intelligence!
In engineering:
It is better to be approximately right
than precisely wrong.
Does it make sense to say that our
project will cost from $796,412.63 to
$804,764.97?
O p e n e d
Fuzzy Logic

Precision vs.
Significance
in the Real
World
Fuzzy Logic
Accuracy Vs
Imprecision
• Describe your reaction whenever you see
an object is falling on someone!!!!!!!!!!
• Accuracy may be killing. What do you will
choose, Importance or Accuracy?

Crisp Logic
Fuzzy Logic• Crisp logic named also as classical or two-
valued logic, in which an object can be
defined only as x or not x
• For example, a person may be defined
only as tall or not; thin or not, etc.
• In another words, an object could belong
only to one set (crisp set)

Crisp Set
Fuzzy Logic• Understanding Fuzzy logic starts with the
concept of a fuzzy set
• To understand what a Fuzzy set, consider
the definition of a Classical or Crisp set
• A classical set is a container that wholly
includes or wholly excludes any given
element

Crisp Set
Fuzzy Logic• In Egypt, we may say, a male adult, is:
• Definitely tall, if height ≥ 180 cm
• Reasonably tall, if 180 > height ≥ 170 cm
• Slightly tall, if 170 > height ≥ 160 cm
• Bit tall, if 160 > height ≥ 145 cm
• Definitely short, if height is < 145 cm

Crisp Set
Fuzzy Logic• A set is a collection of things, for example
room temperature, set of persons tall,
etc….
Short
0

A fuzzy set is a set without a crisp, clearly defined
boundary. It can contain elements with only a
partial degree of membership.
22oC
17oC
40oC
27oC 32oC
41oC
44oC
39oC
46oC
29oC
Set of Hot Temp
Fuzzy Logic
Crisp Vs
Fuzzy Sets

Crisp or classical Representation
Fuzzy Logic
0
1
0
1
10oC 20oC 30oC 40oC 50oC 10oC 20oC 30oC 40oC 50oC
36oC
0.85
Membership
Function
Fuzzy Representation
Crisp Vs
Fuzzy Sets

Basketball team selection (185 cm or more)
0
1
155 165 175 185 195
184 tall
short
Not short
Not that
tall
out in
Fuzzy Logic
Crisp Vs
Fuzzy Sets

Fuzzy Set
Fuzzy Variable
Linguistic
Variable
Fuzzy Logic• Fuzzy sets overlap, an element may
belongs to more than one set with
different degrees of memberships

Can you express the real number (4)
What about
“close to 4” ??
0
1
2 3 4 5 6
0
1
2 3 4 5 6
Classical set
Only Fuzzy can
Linguistic
Variables
Fuzzy Logic
Membership
Functions
Fuzzy Number

What about “more
or less 4” ??
0
1
2 3 4 5 6
What about “very
close to 4” ??
0
1
2 3 4 5 6
= (close to 4) 2
= (close to 4)
Fuzzy Logic
Linguistic
Variables
Fuzzy Hedges

Complete Universe of Age
Age
Young Middle age Old
0 10 20 30 40 50 60 70 80
0
1
Age
Fuzzy Logic

Example: Complete universe of Age
0 10 20 30 40 50 60 70 80
0
1
Age
Young AND middle age (intersection) → min
Fuzzy Logic

0 10 20 30 40 50 60 70 80
0
1
Age
Young OR middle age (union) → max
Fuzzy Logic

0 10 20 30 40 50 60 70 80
0
1
Age
NOT middle age (1-…)
Fuzzy Logic

Example: Complete universe of Age
Now you can express:
More or less young AND not at the
middle age ….→ Natural language
Natural
Language
Linguistic
Variables
Fuzzy Logic

Fuzzy
Reasoning
Mapping
FUZZY INFERENCE ENGINE, reasoning
process: the process of reasoning
from a premise to a conclusion
IF Then
Fuzzy Logic
Knowledge
Base
Decision-Making
Logic
(Decision Rules)
DefuzzificationFuzzificationFuzzy or
Crisp
Input
Crisp
Output
Fuzzy Fuzzy
Fuzzy mapping

If service is poor, then tip is cheap
If service is good, then tip is average
If service is excellent, then tip is generous
Example: Tipping Problem
Tipping value depends on service level
Fuzzy Logic
Fuzzy Rules
Tipping value depends on food goodness
If food is rancid, then tip is cheap
If food is delicious, then tip is generous

Example: Tipping Problem
Tipping value depends on service level and
food goodness
If service is poor or the food is rancid, then tip is
cheap
If service is excellent or food is delicious, then tip
is generous
Fuzzy Logic
Fuzzy Rules

Fuzzy
Membership
Functions
Cheap Average Generous
0% 25%
Rancid Delicious
Poor Good Excellent
Step 1: Fuzzyfication
Quality of Service
Quality of Food
Tips
Fuzzy Logic
0 0.5 1.0
0 0.5 1.0

Example
0% 25%
Rancid DeliciousPoor Good Excellent
Step 2: Apply Rules
Quality of Service Quality of Food Tips
If service is poor or the food is rancid, then tip is cheap
0 0.5 1.0 0 0.5 1.0

Example
Cheap
0% 25%
RancidPoor
Step 3: Rule Firing
If service is poor or the food is rancid, then tip is cheap
OR max
0 0.5 1.0 0 0.5 1.0

Example
0% 25%
Rancid DeliciousPoor Good Excellent
Step 2: Apply Rules
0 0.5 1.00 0.5 1.0

Example
Average
0% 25%
Good
Step 2: Apply Rules
21/09/2020
Emad Elbeltagi
41
0 0.5 1.0 0 0.5 1.0

Example
Generous
0% 25%
DeliciousExcellent
Step 2: Apply Rules
If service is excellent or food is delicious, then
tip is generous
0 0.5 1.00 0.5 1.0

Example
Fuzzy Logic

0% 25%
Tips
Accumulated 3
Rules
Step 4: Defuzzyfication
Fuzzy Output
centroid

Construction
Applications
Fuzzy Logic• Contractors Prequalification
• Selection of Formwork System
• Selection of Excavation Shoring System
• Selection of Project Delivery Approach
• Construction Productivity
• Performance of Construction Projects
• Among many other applications

ARTIFICIAL NEURAL NETWORKS
Predication and Classification

What is it?
Artificial
Neural
Networks
• Artificial neural networks are composed
of interconnecting artificial neurons
(programming constructs that mimic the
properties of biological neurons)

What is it?
Artificial
Neural
Networks
• ANN behave like a human brain
• It is able to learn, recall, and generalize
from training pattern or data
• The processing element in ANN called
“Neuron”
• A human brain consists of 10 billions of
neurons, where each one is connected to
several thousands of other neurons,
similar to the connectivity in ANN

Structure of a
Biological
Neuron
• Dendrites: Accepts Inputs
• Soma: Processes the Inputs
• Axon: Turns the processed
inputs into outputs
• Synapses: The electrochemical
contact between neurons
Artificial
Neural
Networks

Structure of
an Artificial
Neuron
W1
W2
W3
Wn

W0
f
X1
X2
X3
Xn
Axons Synapses Dendrites Body
(Soma)
Axon
Bias
Output
Artificial
Neural
Networks
Inputs

Possible Uses
Artificial
Neural
Networks
• Pattern recognition (face recognition,
radar systems, object recognition),
• Sequence recognition (gesture, speech,
handwritten text recognition)
• Medical diagnosis
• Financial applications
• Stock market analysis
• Forecasting
• Classification

Curve Fitting
Regression
Analysis
Artificial
Neural
Networks
• In Regression analysis, curve fitting is
identifying the model that provides the
best fit to a specific dataset

Example
Artificial
Neural
Networks
• one-layer feed-forward NN
w1
y
b
w2
wn
w3
x2
x1
xn
=
+=
n
i
ii bxwfy
1
)(
Bias ‘b’ is an external
parameter that can be modeled
by adding an extra input

Multi-Layer
ANN
General
Structure
Artificial
Neural
Networks• Multiple layers: Input, hidden and output
• Feed-forward: Output is the input to the
next layer
Input Hidden Output
• Complex non-
linear functions
can be represented

Multi-Layer
ANN
Example
Artificial
Neural
Networks
• 3-layer feed-forward network
xn
x1
x2
Hidden layersInput Layer Output Layer
y1
y2
ym
=
+=
n
j
ijiji bxwfy
1
)(

ANN Training
Artificial
Neural
Networks• Training means identifying the connection
weights
• The data set is divided into two portions
for training and testing.
• The training allows the NN to generalize
and predict.
• Avoid under or over training.

Memorization
Vs Learning
Fuzzy Logic• The NN behaves well in training phase
• In the testing phase, not able to predict

Types of
Learning
Machine
Learning
Artificial
Neural
Networks• Supervised learning: have a teacher,
inputs and outputs are known
• Unsupervised learning: no teacher,
outputs are unknown
• Reinforcement learning: no detailed
instruction, only the final reward is
available

Construction
Applications
Fuzzy Logic• Estimating Construction Costs of Various
Types of Projects
• Predicting the Performance of
Construction Companies
• Estimating Cost Contingency
• Risk Assessment
• Dispute Resolution
• Construction Productivity

EVOLUTIONARY ALGORITHMS
Optimization Problems

What is it?
Evolutionary
Algorithms• Evolutionary algorithms (EAs) are
stochastic search methods that mimic
the natural biological evolution and/or
the social behavior of species
• Such algorithms have been developed to
arrive at near-optimum solutions to
large-scale optimization problems

What is it?
Evolutionary
Algorithms• EAs operate on a set of individuals
(population)
• Each individual is a potential solution to
the problem being solved
• The population is randomly generated
• Every individual in the population is
assigned a measure of goodness with
respect to the considered problem
(fitness function)

What is it?
Evolutionary
Algorithms• This value is the quantitative information
the algorithm uses to guide the search
• Examples include: Genetic Algorithms,
Memetic Algorithms, Swarm
Optimization, Ant Colony, …….

What is it?
Evolutionary
Algorithms

What is it?
Evolutionary
Algorithms• EAs are blind, they need only an
objective function and decision variables
• No derivatives are needed
• They follow random search, however
guided search system
• They can work with continuous, discrete,
integer, and mixed decision parameters

Who EAs
work?
Evolutionary
Algorithms• Problem coding
• Create population
• Cyclic Process:
• Reproduction, selection of the fittest
• Shuffling, Mutation, Recombination
• Reinsertion into the population

Genetic
Algorithms
Evolutionary
Algorithms• Genetic algorithms (GAs) is the first
developed EAs technique
• A solution is created in the form of a
string, called “chromosome”, consisting
of elements, called “genes”, hold a set of
values for the optimization variables

Genetic
Algorithms
Evolutionary
Algorithms• The chromosome represents a feasible
solution for the problem
• The length of the chromosome equals
the number of variables
1 2 3 . . . . . . . . . . . . . . . . . N
Variable value
Number of variables
78 18 52 2 61. . . .97 36 30 44 3

Genetic
Algorithms
Evolutionary
Algorithms• Create population at random
• Calculate the fitness of each chromosome
• Calculate the relative fitness of each
chromosome
• Apply the GA operators
• Reproduction
• Crossover
• Mutation

Genetic
Algorithms
Reproduction
Evolutionary
Algorithms• Each individual has a rank based on its
objective function in relative to the sum
of the objective function values for the
whole population
• For example, having 3 individuals with
objective function equal: 5, 10, 25 Then
the relative fitness of the first is 5/40 =
0.125, the second is 10/40 = 0.25, and the
third is 25/40 = 0.625

Evolutionary
Algorithms
• Roulette Wheel Selection
• Individuals with higher objective function
values have a higher proportional fitness
• These chromosomes will have a higher
probability of selection
Genetic
Algorithms
Reproduction

Evolutionary
Algorithms
• Exchange of individuals variables
• These individuals are called parents and
the resulted individual called offspring
Genetic
Algorithms
Crossover
Parent gene (B)
A2 B3 B4 B5 A6A1
B2 B3 B4 B5 B6B1
A2 A3 A4 A5 A6A1Parent gene (A)
Generate random point (e.g., 3)
B2 A3 A4 A5 A6B1
Offspring (child) (A)
Offspring (child) (B)

Evolutionary
Algorithms
• Mutation resembles the process of a
sudden generation of an offspring that
turns to be a genius
• It breaks stagnation in the evolutionary
process, avoiding local minima
• If one important information is missing,
mutation may introduce this missed
information
Genetic
Algorithms
Mutation

Example
Evolutionary
Algorithms• 10-memebr truss, find the members cross
sectional area so as to minimize the truss
cost to support the load, stress and strain
constraints must be satisfied.

Example
Evolutionary
Algorithms• Binary representation of the cross
sectional area, 4-bits element (0 0 0 0)
• (0 1 1 1) a 7-inch cross section
• Compute the weight and cost of the truss
• Stresses are computed using appropriate
methods of structural analysis

Applications
Evolutionary
Algorithms• Resource allocation
• Time-cost trade off
• Structural optimization
• Pavement management systems
• Design of water distribution systems
• Optimum mark-up estimation
• Optimization of Neural Network weights
• And many other optimization problems

Artificial Intelligence and Fuzzy Logic Applications in Construction

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