Artificial intelligence in civil engineering

SEMINAR REPORT
ON
ARTIFICIAL INTELLIGENCE IN CIVIL
ENGINEERING
SUBMITTED BY
ASEENA.L
MCT15CE022
DEPARTMENT OF CIVIL ENGINEERING
MOHANDAS COLLEGE OF ENGINEERING AND
TECHNOLOGY
ANAD, NEDUMANGAD
THIRUVANANTHAPURAM-695544

MOHANDAS COLLEGE OF ENGINEERING AND
TECHNOLOGY
ANAD, NEDUMANGAD
THIRUVANANTHAPURAM-695544
DEPARTMENT OF CIVIL ENGINEERING
2018
Bonafide Certificate
This is to certify that the bonafide report of seminar taken by ASEENA.L
(MCT15CE022) Department of Civil Engineering is in partial fulfillment
of the requirement for the award of the degree of B.Tech in Civil
Engineering of APJ Abdul Kalam Technological University.
Guide: Ms. Sreegowri G R HOD
Assistant Professor Dr.Narayanan S
Place : Anad
Date :

ACKNOWLEDGEMENT
I express my sincere gratitude to my guide Ms. Sreegowri G R, Assistant
Professor, Department of Civil Engineering, MCET, Trivandrum, for the valuable
guidance, constant encouragement and creative suggestions offered during the course of
this seminar and also in preparing this report.
I express my sincere thanks to Dr. Narayanan S, Professor and Head,
Department of Civil Engineering, MCET, Trivandrum, for his valuable support and
advice.
I express my sincere thanks to Ms. Aparna S R and Ms. Remya, Staff Advisors
and Seminar Co-ordinators, Department of Civil Engineering, MCET, Trivandrum for
providing necessary facilities and for their sincere co-operation.
I extend my sincere thanks to all faculty members of the Department of Civil
Engineering and my friends for their help and support.
Above all I thank God Almighty for his grace without which it would not have
been possible to complete this work in time.
DATE : ASEENA.L

ABSTRACT
Artificial intelligence is a branch of computer science, involved in the research,
design, and application of intelligent computer. Traditional methods for modeling and
optimizing complex structure systems require huge amounts of computing resources,
and artificial intelligence based solutions can often provide valuable alternatives for
efficiently solving problems in civil engineering. This seminar summarizes recently
developed methods and theories in the developing direction for applications of artificial
intelligence in civil engineering. The field of artificial intelligence, or AI, attempts to
understand intelligent entities as well as construct them to make the operation
reasonably simple and easy, correct and precise. Artificial neural networks are typical
examples of a modern interdisciplinary subject. Sophisticated modeling technique that
can be used for solving many complex problems serves as an analytical tool for
qualified prognoses of the results. Using the concept of the artificial neural networks
and the results of the performed numerical analyses make the field of civil engineering
more accurate, precise and efficient especially in the fields of smart materials and many
more.

CONTENTS
Page No.
LIST OF FIGURES
LIST OF NOTATIONS
1. INTRODUCTION 1
2. DEVELOPMENT OF ARTIFICIAL INTELLIGENCE 2
3. INTELLIGENT OPTIMIZATION METHODS IN CIVIL ENGINEERING 3
3.1. EVOLUTIONARY COMPUTATION 4
3.1.1 Genetic algorithms 4
3.1.2 Artificial immune system 5
3.1.3 Genetic programming 5
3.1.4 Other evolutionary algorithms 6
4. APPLICATION OF ARTIFICIAL INTELLIGENCE IN CIVIL
ENGINEERING 7
5. FUTURE TRENDS 17
6. ADVANTAGES 19
7. DISADVANTAGES 20
8. CONCLUSION 21
9. REFERENCES 22

LIST OF FIGURES
Fig. No. Title Page No.
1 Neural network system 9
2 Neuroform 10
3 Belief network 11
4 Initial design process 12
5 Intelligent planning 13
6 Construction robot fleet management system 14
7 Bridge planning using GIS and expert system approach 15

LIST OF NOTATIONS
1. AI - Artificial Intelligence
2. AN - Artificial Neural Network
3. SC - Self Compacting Concrete
4. HPC - High Performance Concrete
5. MDD - Maximum Dry Density
6. UCS - Unconfined Compressive Strength
7. EC - Evolutionary Computation
8. GA - Genetic Algorithm
9. AIS - Artificial Immune System
10. GP - Genetic Programming
11. SA - Simulated Annealing
12. GOT - Genetic Operation Tree
13. OT - Operation Tree
14. PSO - Particle Swarm Optimization
15. ACA - Ant Colony Algorithm
16. TMD - Tuned Mass Damper
17. MR - Magneto Rheological
18. RST - Rough Set Theory
19. CUPSO - Center Unified Particle Swarm Optimization
20. FD - Finite Difference
21. IACO - Improved Ant Colony Optimization

22. MDOF - Multiple Degree Of Freedom
23. ANN-HC - Artificial Neural Network based Heat Convection
24. OK - Ordinary Kriging
25. IDW - Inverse Distance Weighing
26. NN - Neural Network
27. FRF - Frequency Response Functions
28. EFHNN - Evolutionary Fuzzy Hybrid Neural Network
29. HONN - High Order Neural Networks
30. HNN - Hybrid Neural Networks
31. FPNN - Fuzzy Polynomial Neural Networks
32. AVC - Active Vibration Control
33. EFNIS - Evolutionary Fuzzy Neural Inference System
34. SMA - Shape Memory Alloy
35. AFSMC - Adaptive Fuzzy Sliding Model
36. LRB - Lead Rubber Bearing
37. FLC - Fuzzy Logic Controller
38. AHP - Analytic Hierarchy Process
39. SSI - Soil Structure Interaction
40. KBS - Knowledge Based System
41. SIPE - System For Interactive Planning And Execution
42. CREMS - Construction Robotic Equipment Management System

Artificial intelligence in civil engineering
Dept. of civil engineering MCET
1. INTRODUCTION
The research of artificial intelligence has been developed since 1956, when the term
“Artificial Intelligence, AI” was used at the meeting hold in Dartmouth College, USA.
Artificial intelligence, a comprehensive discipline, was developed based on the
interaction of several kinds of disciplines, such as computer science, cybernetics,
information theory, psychology, linguistics, and neurophysiology. Artificial intelligence
is a branch of computer science, involved in the research, design and application of
intelligent computer. The goal of this field is to explore how to imitate and execute
some of the intelligent function of human brain, so that people can develop technology
products and establish relevant theories. The first step: artificial intelligence’s rise and
fall in the 1950s. The second step: as the expert system emerging, a new upsurge of the
research of artificial intelligence appeared from the end of 1960s to the 1970s. The third
step: in the 1980s, artificial intelligence made a great progress with the development of
the fifth generation computer. The fourth step: in the 1990s, there is a new upsurge of
the research of artificial intelligence: with the development of network technology,
especially the international internet technology, artificial intelligence research by a
single intelligent agent began to turn to the study of distributed artificial intelligence
based on network environment. The main theories and methods of artificial intelligence
are summarized as symbolism, behaviorism, and connectionism approach. Now we will
elaborate the latest progress of artificial intelligence technology in all aspects of civil
engineering and their relationship as follows.

2. DEVELOPMENT OF ARTIFICIAL INTELLIGENCE
The term Artificial Intelligence was coined by John McCarthy in his attempts to
describe the process of human thinking as a mechanical manipulation of symbols in the
1940s. the main constituents of soft computing are neural networks, evolutionary
algorithms, probability reasoning and fuzzy-logic. The potential applications of
Artificial Neural Networks in the field of Civil engineering includes the use of ANN’s
in designing, planning, construction, and management of infrastructures such as
highways, bridges, airports, railroads, buildings, dams, and utilities. ANN’s have been
applied to predict tender bids, construction cost and construction budget performance.
AI has role in project cash flow, maintenance construction demand and labour
productivity. The genetic algorithm particularly employed in the field of structural
optimization and in the allocation of resources in the building problems. The
optimization of road infrastructure and water channel nets, for the analysis and the
planning of long suspension bridges and to define better load scenarios and structural
performances, genetic algorithms can be employed. The fuzzy logic finds remarkable
applications in the field of civil engineering such as the demand of analysis in presence
of uncertainties like control techniques, structural reliability and handling uncertainty in
materials. ANN’s have been used to conduct crane type and model selection, the model
was developed and tested for cost estimating for RCC structures and employed a
framework which employs Neural Networks to plan the work breakdown structure for
project.

3. INTELLIGENT OPTIMIZATION METHODS IN CIVIL
ENGINEERING
Artificial intelligence is a science on the research and application of the law of the
activities of human intelligence. It has been a far-reaching cross-frontier subject, after
the 50 years’ advancement. Nowadays, this technology is applied in many fields such as
expert system, knowledge base system, intelligent database system, and intelligent robot
system. Expert system is the earliest and most extensive, the most active and most
fruitful area, which was named as “the knowledge management and decision-making
technology of the 21st
century.” This knowledge and experience are illogically
incomplete and imprecise, and they cannot be handled by traditional procedures.
However, artificial intelligence has its own superiority. It can solve complex problems
to the levels of experts by means of imitate experts. All in all, artificial intelligence has
a broad application prospects in the practice of civil engineering.
Adam and Smith presented progress in the field of adaptive civil-engineering
structures. Self-diagnosis, multi-objective shape control, and reinforcement-learning
processes were implemented within a control framework on an active tensegrity
structure. Among artificial intelligence-based computational techniques, adaptive neuro-
fuzzy inference systems were particularly suitable for modeling complex systems with
known input-output data sets. Such systems can be efficient in modelling nonlinear,
complex, and ambiguous behavior of cement-based materials undergoing single, dual,
or multiple damage factors of different forms in civil engineering. Bassuoni and Nehdi
developed neuro-fuzzy based prediction of the durability of self-consolidating concrete
to various sodium sulfate exposure regimes. Prasad et al. presented an artificial neural
network (ANN) to predict a 28-day compressive strength of a normal and high strength
self-compacting concrete (SCC) and high performance concrete (HPC) with high
volume fly ash. Lee et al. used an artificial intelligence technique of back-propagation
neural networks to assess the slope failure. The numerical results demonstrate the
effectiveness of artificial neural networks in the evaluation of slope failure potential.
Shaheen et al. presented a proposed methodology for extracting the information from
experts to develop the fuzzy expert system rules, and a tunneling case study was used to
illustrate the features of the integrated system. Das et al. described two artificial
intelligence techniques for prediction of maximum dry density (MDD) and unconfined

compressive strength (UCS) of cement stabilized soil. Forcael et al. presented the
results of a study that incorporates computer simulations in teaching linear scheduling
concepts and techniques, in a civil engineering course “Construction Planning and
Scheduling.” To assess the effect of incorporating computer simulation in teaching
linear scheduling, the students’ evaluations and answers to the questionnaire were
statistically compared. Krcaronemen and Kouba proposed a methodology for designing
ontology-backed software applications that make the ontology possible to evolve while
being exploited by one or more applications at the same time. The methodology relies
on a contract between the ontology and the application that is formally expressed in
terms of integrity constraints. In addition, a reference Java implementation of the
methodology and the proof-of-concept application in the civil engineering domain was
introduced.
Due to a lot of uncertain factors, complicated influence factors in civil engineering,
each project has its individual character and generality; function of expert system in the
special links and cases is a notable effect. Over the past 20 years, in the civil
engineering field, development and application of the expert system have made a lot of
achievements, mainly used in project evaluation, diagnosis, decision-making and
prediction, building design and optimization, road and bridge health detection and some
special field, and so forth.
3.1 EVOLUTIONARY COMPUTATION
Evolutionary computation (EC) is a subfield of artificial intelligence, which uses
iterative process (often inspired by biological mechanisms of evolution) to evolve a
population of solution to a desired end. EC has been applied to the domain of civil
engineering for several decades, mainly served as an effective method for solving
complex optimization problems.
3.1.1 Genetic algorithms
Genetic algorithms (GAs) are one of the famous evolutionary algorithms which
simulate the Darwinian principle of evolution and the survival of the fittest in
optimization. It has extensive application value in the civil engineering field, but in
many aspects it needs to be further studied and improved. According to the research
progress above the genetic algorithm in civil engineering, due to genetic algorithm

developed rapidly, so there are still a lot of improvement measures not included in this
paper.
In recent years, the improvement of the genetic algorithm introduced many new
mathematical tools and absorbed civil engineering as the latest achievement of
applications. Along with the computer technology, the genetic algorithm in civil
engineering application will be more general and more effective.
Senouci and Al-Derham presented a genetic-algorithm-based multiobjective
optimization model for the scheduling of linear construction projects. The model allows
construction planners to generate and evaluate optimal/near-optimal construction
scheduling plans that minimize both project time and cost.
3.1.2 Artificial immune systems
Provoked by the theoretical immunology, observed immune functions, principles,
and models, artificial immune system (AIS) stimulates the adaptive immune system of a
living creature to unravel the various complexities in real-world engineering
optimization problems. In this technique, a combination of the genetic algorithm and the
least-squares method was used to find feasible structures and the appropriate constants
for those structures. The new approach overcomes the shortcomings of the traditional
and artificial neural network-based methods presented in the literature for the analysis
of civil engineering systems.
Dessalegne and Nicklow employed an artificial life algorithm, derived from the
artificial life paradigm. The resulting multi-reservoir management model was
successfully applied to a portion of the Illinois River Waterway, Chicago.
3.1.3 Genetic Programming
Genetic programming is a model of programming which uses the ideas of biological
evolution to handle complex optimization problems. Aminian et al. presented a new
empirical model to estimate the base shear of plane steel structures subjected to
earthquake load using a hybrid method integrating genetic programming (GP) and
simulated annealing (SA), called GP/SA. Hsie et al. proposed a novel approach, called
“LMGOT,” that integrates two optimization techniques: the Levenberg Marquardt (LM)
Method and the genetic operation tree (GOT). The GOT borrows the concept from the
genetic algorithm, a famous algorithm for solving discrete optimization problems, to
generate operation trees (OTs), which represent the structures of the formulas. Results

show a concise formula for predicting the length of pavement transverse cracking and
indicate that the LMGOT was an efficient approach for building an accurate crack
model.
Cevik and Guzelbey presented two plate strength formulations applicable to metals
with nonlinear stress-strain curves, such as aluminum and stainless steel alloys, obtained
by neural networks and Genetic Programming. The proposed formulations enable
determination of the buckling strength of rectangular plates in terms of Ramberg-
Osgood parameters.
3.1.4 Other evolutionary algorithms
Caicedo and Yun proposed an evolutionary algorithm that was able to identify both
global and local minima. The proposed methodology was validated with two numerical
examples.
Khalafallah and Abdel-Raheem developed a novel evolutionary algorithm named
Electimize and applied it to solve a hard optimization problem in construction
engineering. The algorithm mimics the behavior of electrons flowing through electric
circuit branches with the least electric resistance. On the test problem, solutions are
represented by electric wires and are evaluated on two levels: a global level, using the
objective function, and a local level, evaluating the potential of each generated value for
every decision variable. The experimental results show that Electimize has good ability
to search the solution space extensively, while converging towards optimality.
Ahangar-Asr et al. presented a new approach, based on evolutionary polynomial
regression (EPR), for analysis of stability of soil and rock slopes. Rezania et al.
presented another method based on EPR for capturing nonlinear interaction between
various parameters of civil engineering systems.

4. APPLICATION OF ARTIFICIAL INTELLIGENCE IN CIVIL
ENGINEERING
Artificial Intelligence methods have been extensively used in the fields of civil
engineering applications e.g. construction management, building materials, hydraulic
optimization, geotechnical and transportation engineering and newly added EHS. Over
the past 20 years in the civil engineering field, development and application of the
expert system have made a lot of achievements, mainly used in project evaluation,
diagnosis, decision-making and prediction, building design and optimization, the project
management construction technology, road and bridge health detection and some
special field and so forth.
Among AI based computational techniques, adaptive neuro-fuzzy inference systems
were particularly suitable for modelling complex systems with known input-output data
sets especially to study the behavior of cement-based materials undergoing single, dual,
or multiple damage factors. The model allows construction planners to generate and
evaluate optimal/near-optimal construction scheduling plans that minimize both project
time and cost.
AI also helps in development of robots and automated systems. Even the role of
artificial intelligence is also reported in the case of smart materials. The smart system
refers to a device which can sense changes in its environment and can make an optimal
response by changing its material properties, geometry, mechanical or electromagnetic
response. Both the sensor and the actuator function with their appropriate feedback must
be properly integrated.
1. Structural health monitoring
Embedding sensors within structures to monitor stress and damage can reduce
maintenance costs and increase the lifespan. This is already being used in over forty
bridges worldwide
2. Self repair materials
It involves embedding thin tubes containing uncured resin into materials. When
damage occurs, these tubes break, exposing the resin which fills any damage and
sets. Self repair could be important in inaccessible environments such as underwater
or in space.

3. Structural engineering
In the field of structural engineering, they are used to evaluate durability. Not only
the smart materials or structures are restricted to sensing but also they adapt to their
surrounding environment such as the ability to move, vibrate and demonstrate
various other responses as well as for monitoring the integrity of bridges, dams,
offshore oil-drilling towers where fiber-optic sensors embedded in the structures are
utilized to identify the trouble areas.
4. Waste management
Manual disassembly of the waste is a challenging, expensive and time consuming
task but the use of smart materials could help to automate the process. Even it shows
a role in food waste management.
5. Concrete mix design
Concrete mix design is difficult and sensitive. The concrete mix design is based on
the principles of workability of fresh concrete, desired strength and durability of
hundred concrete which in turn is governed by water cement ratio law. The strength
of the concrete is determined by the characteristics of the mortar, course aggregate,
and the interface. For the same quality mortar, different type of course aggregate
with different shape, texture, minerology, and strength may result in different
concrete strengths.
6. Estimation
Artificial neural networks(ANN’s) are mostly suited for developing decision aids
with analogy-based problem solving capabilities in estimation.
7. Analogy-based solution to markup estimation problem
In 1994 Professors Tarek Hegazy and Osama Moselhi published a technical paper,
which presented a methodology for deriving analogy-based solutions to a class of
unstructured problems in civil engineering. Such problems have identifiable
characteristics, including: problems frequently require simultaneous assessment of a
large number of quantitative as well as qualitative factors that influence the solution;
traditional algorithmic and reasoning-intensive techniques are not adequate to model
the problem; solutions are devised in practice primarily based on analogy with
previous cases coupled with a mixture of intuition and experience; and domain
knowledge is mostly implicit and very difficult to be extracted and described.
For this class of problems, artificial neural networks are most suited for developing
decision aids with analogy-based problem-solving capabilities. A methodology is

presented and used to develop a practical model for mark-up estimation using
knowledge acquired from contractors in Canada and the U.S. The model design,
training, and testing are described along with the generalization improvements made
using the genetic algorithms technique.
8. Neuromodex – Neural Network System for Modular Construction Decision
Making
Fig 1: Neural network system
( Source: http://ai.business/2016/11/23/10-use-cases-of-ai-in-the-field-of-
construction)
The model helps make a decision whether to use a conventional “stick-built”
method or to use some degree of modularization when building an industrial process
plant. This decision is based on several decision attributes which are divided into
following five categories: plant location, environmental and organizational, labour-
related, plant characteristics, and project risks.
The neural network is trained using cases collected from several engineering and
construction firms and owner firms of industrial process plants. In this paper, an
overview of modular construction is provided and the reasons for using a neural
network are also discussed. The architecture, representation, and training procedure
for the selected neural network paradigms are described. The performance of the
trained neural network system is compared with the recommendations provided by
human experts.

9. Neuroform – Neural Network System for Vertical Formwork Selection
Fig 2: Neuroform
(Source: http://ai.business/2016/11/23/10-use-cases-of-ai-in-the-field-of-
construction)
Neuroform is a computer system that provides the selection of vertical formwork
systems for a given building site. The reasons for choosing a neural network
approach instead of a traditional expert system are discussed. The selection of an
appropriate neural network model, its architecture, representation of the network
training examples, and the network training procedure are described. The details of
the user interaction with the trained neural network system are presented. The
performance of Neuroform is validated comparing its recommendations with that of
Wallform, a rule-based expert system for vertical formwork selection. A statistical
hypothesis test, conducted on the recommendations of Neuroform when partial
inputs are given, demonstrates the system’s fault-tolerant and generalization
properties.

10. Belief Networks for Construction Performance Diagnostics
Fig 3: Belief network
(Source: http://ai.business/2016/11/23/10-use-cases-of-ai-in-the-field-of-
construction)
Belief networks, also referred to as Bayesian networks, are a form of artificial
intelligence that incorporates uncertainty through probability theory and conditional
dependence. Variables are graphically represented by nodes, whereas conditional
dependence relationships between the variables are represented by arrows. A belief
network is developed by first defining the variables in the domain and the
relationships between those variables. The conditional probabilities of the states of
the variables are then determined for each combination of parent states. During
evaluation of the network, evidence may be entered at any node without concern
about whether the variable is an input or output variable.
An automated approach for the improvement of the construction operations
involving the integration of the belief networks and computer simulation is
described. In this application, the belief networks provide diagnostic functionality to
the performance analysis of the construction operations. Computer simulation is
used to model the construction operations and to validate the changes to the
operation recommended by the belief network.
11. Building KBES for Diagnosing PC Pile with Artificial Neural Network
Diagnosis of damage of prestressed concrete piles during driving is an important
problem in foundation engineering. An effort to build an expert system for the
problem is described in this paper. To overcome the bottleneck of knowledge
acquisition, an artificial neural network is used as the learning mechanism to
transfer engineering experience into usable knowledge. The back- propagation

learning algorithm is employed to train the network for extracting knowledge from
training examples.
The influences of various control parameters (including learning rate and
momentum factor) and various network architecture factors (including the number
of hidden units and the number of hidden layers) are examined. The results prove
that the artificial neural network can work sufficiently as a knowledge-acquisition
tool for the diagnosis problem. To apply the knowledge in the trained network, a
reasoning strategy that hybridizes forward-and backward-reasoning schemes is
proposed to realize the inference mechanism.
12. Modelling Initial Design Process using Artificial Neural Networks
Fig 4: Initial design process
construction)
The preliminary design model is of vital importance in the synthesis of a finally
acceptable solution is a design problem. The initial design process is extremely
difficult to computerize because it requires human intuition. It has often been
impossible to form declarative rules to express human intuition and past experience.
The suitability of an artificial neural network for modelling an initial design process
has been investigated in this paper.
Development of a network for the initial design of reinforced-concrete rectangular
single-span beams has been reported. The network predicts a good initial design
(i.e., tensile reinforcement required, depth of beam, width, cost per meter, and the
moment capacity) for a given set of input parameters (i.e., span, dead load, live load,

concrete grade, and steel type). Various stages of development and performance
evaluation with respect to a rate of learning, fault tolerance, and generalization have
been presented.
13. Intelligent Planning of Construction Projects
Knowledge representation and reasoning techniques derived from artificial
intelligence research permit computers to generate plans, not merely analyse plans
produced by humans. They explicitly represent knowledge about how to generate
plans in the form of initial and goal states, descriptions of actions along with their
preconditions and effects, and a control structure for selection new actions to insert
into a project plan.
Researchers Kartam and Levitt, have chosen the system for interactive planning and
execution (SIPE) to investigate the utility of AI planners for construction project
planning. They were modelling a multistory office building project for construction
planning, implementing SIPE to plan this project, and describing SIPE’s
performance in planning the construction of large-scale multistory buildings. With
the use of a frame hierarchy, generic operators, and a constraint-based approach,
SIPE can generate logically correct activity networks for multistory building
construction from a description of the components of a facility. To model such
construction projects in a concise and uniform framework, they showed the
usefulness of some underlying principles for establishing ordering relationships
among the project components involved in construction activities.
Fig 5: Intelligent planning
( Source: P6 project/Apartment )

14. Construction Robot Fleet Management System
The application of robotic equipment to the execution of construction tasks is
gaining attention by researchers and practitioners around the world. A number of
working prototype systems have been developed by construction companies or
system manufacturers, and implemented on construction job sites. Several Japanese
construction firms have already developed their own fleet of construction robots. In
1991 Skibniewski and Russell described a HyperCard prototype of the construction
robotic equipment management system (CREMS), developed as a response to the
need to effectively manage diverse robots on future construction sites.
The utility of this system lies in optimizing the robot performance of work tasks on
as many construction projects in a contractor’s portfolio as feasible. Thus, economic
benefits of robot use can be achieved more easily. Thus, robot development costs
can be recovered faster, and robot use can be distributed over more applications and
types of construction tasks.
Fig 6: Construction robot fleet management system
construction)
15. Bridge Planning Using GIS and Expert System Approach
In the planning process of a new road network, the planner should consider possible
locations of bridges and tunnels. The selection of the best alignment imposes the
need to investigate the effect of the location of each bridge on the bridge type that
fits this location. This task has not been done so far because of the large volume of
data needed and the complicated interaction between many factors. Considering this

task in the early stage of road alignment planning can result in a more rational
design.
Geographic information systems and expert systems are proposed as two
methodologies that can help in comparing candidate sites and candidate types
simultaneously. Having this computation power, quantitative comparison can be
done faster and much more precisely than in the case of conventional simplified
methods. This can result in improving the design of the road network in general and
in having bridges designed to meet the requirements of erection, maintenance,
driving comfort, and landscape.
Fig 7: Bridge planning using GIS and expert system approach
construction)
16. Artificial Neural Network Approach for Pavement Maintenance
The major objective is to assist decision makers in selecting an appropriate
maintenance and repair action for a defected pavement. This is typically performed
through collecting condition data, analyzing and selecting appropriate maintenance
and repair actions.
17. ANN for EHS
For EHS, there are multiple areas where AI can contribute. Imagine a robot carrying
out tasks in construction – near misses and accidents would potentially be zero
because of the lack of human errors (dropping something, deciding to answer the

phone at the wrong time, coffee breaks). But there is a need for both innovation and
governance going forward for the effective OSHA.
18. Tidal forecasting
Tidal level record is an important factor in determining constructions or activity in
maritime areas. Kalman (1960) proposed the Kalman filtering method to calculate
the harmonic parameters instead of the least squares analysis. Mizumura (1984) also
proved that the harmonic parameters using the Kalman filtering method could be
easily determined from only a small amount of historical tidal records and can be
used for tide level forecasting.
19. Earthquake-Induced Liquefaction
During the occurrence of earthquakes, numerous civil structures, such as buildings,
highway embankments and retaining structures have been damaged or completely
destroyed. The damage of civil structures occurs in two modes; the first mode is that
of structural failure and the second mode is that of foundation failure, caused by
liquefaction. Therefore, estimation of the earthquake-induced liquefaction potential
is essential for the civil engineers in the design procedure. Artificial intelligence
immensely helps in the design of structures to safeguard against the earthquakes.

5. FUTURE TRENDS
It is deserved to note that swarm-based methods and artificial immune systems are
not yet mature and thus are expected to gain more research interests. In civil
engineering field, in the present situation, the research and development of artificial
intelligence is only just starting, so far failing to play its proper role. The combination
including Artificial intelligence technology and object-oriented and the Internet is the
artificial intelligence technology the general trend of development. Artificial
intelligence is in its development for civil engineering in the following aspects.
1. Fuzzy processing, integrated intelligent technology, intelligent emotion technology
in the civil engineering.
2. To deepen the understanding of the problems of uncertainty and to seek appropriate
reasoning mechanism is the primary task. To develop practical artificial intelligence
technology, only to be developed in the field of artificial intelligence technology,
and the knowledge to have a thorough grasp.
3. According to application requirements of civil engineering practical engineering, the
research and development of artificial intelligence technology in civil engineering
field were carried out continually. Many questions in civil engineering field need to
used artificial intelligence technology. Due to the characteristics of civil engineering
field, artificial intelligence technology was used in many areas for civil engineering
field, such as civil building engineering, bridge engineering, geotechnical
engineering, underground engineering, road engineering, geological exploration and
structure of health detection, and so forth.
4. Hybrid intelligence system and a large civil expert system research.
5. With the development of artificial intelligence technology, some early artificial
intelligence technology need enhance and improve for knowledge, reasoning
mechanism and man-machine interface optimization, and so forth.
6. To some related problems, many single function of artificial intelligent system
integration can carry out, integrated as a comprehensive system of artificial
intelligence, and expand the artificial intelligence system to solve the question
ability.
7. Artificial intelligence technology was used in the actual application, only in the
practical application of artificial intelligence technology, to test the reliability and

give full play to the role of the artificial intelligence technology and to make
artificial intelligence technology to get evolution and commercialize. In the
commercialization of artificial intelligence technology, there are many successful
examples abroad, for enterprise and socially brought considerable benefit.

6. ADVANTAGES
1. Reduce the risk of accidents in the workplace
Since construction and engineering can be a dangerous industry, some of the riskiest
jobs can be replaced by robots. When programmed correctly, they can be designed
to learn from interactions within it's surroundings and operate in dangerous
environments, resulting in less work-related injuries. Although automation was
originally used to increase productivity on construction sites, it’s beginning to prove
that the workplace can also be made safer through AI.
2. Not affected by hostile environments
Intelligent robots have the ability to complete dangerous construction tasks. These
may include lifting heavy equipment, digging fuels that could otherwise be hostile
for humans, space exploration and enduring problems that could injure or kill
humans. Robots can never refuse to do a task, or be distracted by colleagues in the
workplace.
3. Can replace tiresome tasks
Repetitive, tedious or dangerous jobs can be completed by machine intelligence.
They are stronger and faster than humans and can work on tasks 24/7 without
getting tired or bored. The human brain can become tired and less focused if worked
continuously for too long, increasing the risk of accidents in the workplace. Robots
will never get tired of what they are programmed to do, and can be used where
human safety is a concern.
4. Didn’t need breaks
Robots do not require lunch breaks, holidays, sick days or wages. They can be set to
work on a repetitive cycle, unless programmed otherwise. As long as the machine is
maintained and programmed correctly, it can work without stopping. This helps
businesses to achieve tight deadlines with 24/7 production, letting operators do the
more skilled tasks which require a lot of fitness and experience.

7. DISADVANTAGES
1. Can be very expensive
Maintaining a robot can be extremely expensive as they are very complex machines
which require huge costs to repair and maintain. They have software programmes
that need frequent upgrading to be able to achieve the needs of the constantly
changing environment. It is not an easy or cheap task to get a machine to do your
job. Therefore, only organisations which can afford them will be able to invest.
2. Not able to work outside of what they are programmed to do
Robots can only do the work that they are programmed to do. They are not able to
act any differently outside of the programming which is stored in their internal
circuits and firmware. When it comes to creativity, nothing can beat a human mind.
A computer can’t think differently while drawing, building or completing a task on
a construction site. A machine can’t think ‘outside the box’ whereas thousands of
new thoughts and ideas comes into a human mind every day.
3. Unemployment may rise
Experts are debating the impact AI can have on the job market and whether it’s
something we should welcome or fear. Even with computing technologies
constantly improving and industrial robots becoming more advanced, jobs may be
destroyed faster than they’re created. It’s estimated by MIT economist Erik
Brynjolfsson that the vast majority of employment is likely to continue to
dramatically drop over the next decade.
4. Robots do not get better with experience…yet
Unlike humans; AI cannot be improved with experience. Machines may be able to
store enormous amounts of data, but the storage, is not as effective as the human
brain and with time, can lead to wear and tear. It stores a lot of data but the way it
can be accessed and used is very different from human intelligence.

8. CONCLUSION
1. Artificial Intelligence has been successfully applied to many civil engineering areas
like prediction, risk analysis, decision-making, resources optimization, classification
and selection etc.
2. The artificial intelligence in civil engineering plays a major role in constructing,
maintaining and managing different aspects of civil engineering problems.
3. AI has shown its potency to perform better than the conventional methods.
4. AI has a number of significant benefits that make them a powerful and practical tool
for solving many problems in the field of civil engineering and are expected to be
applicable in near future by using sophisticated instruments based on the algorithms
and database to reduce the efforts and cost of construction and management.
5. Artificial intelligence can help inexperienced users solve engineering problems, can
also help experienced users to improve the work efficiency, and also in the team
through the artificial intelligence technology to share the experience of each
member.
6. Artificial intelligence technology will change with each passing day, as the
computer is applied more and more popularly, and in civil engineering field will
have a broad prospect.

9. REFERENCES
1. Akshata Patil, Lata Patted, Mahesh Tenagi, Vaishnavi Jahagirdar, Madhuri Patil and
Rahul Gautam(2017), “Artificial Intelligence as a Tool in Civil Engineering- A
Review”, IOSR Journal of Computer Engineering
2. Artificial intelligence, 2012, http://en.wikipedia.org/wiki/Artificial_intelligence.
3. Pengzhen Lu, Shengyong Chen and Yujun Zheng (2012), “Artificial intelligence in
Civil engineering, Mathematical Problems in Engineering”, Volume 2012, Article
ID 145974, 22 pages
http://dx.doi.org/10.1155/2012/145974
4. P. Krcaronemen and Z. Kouba, “Ontology-driven information system design,” IEEE
Transactions on Systems, Man and Cybernetics C, vol. 42, no. 3, 2012.
5. Jeng, D. S.; Cha, D. H.; Blumenstein, M. “Application of Neural Networks in Civil
Engineering Problems.” // Proceedings of the International Conference on Advances
in the Internet, Processing, Systems and Interdisciplinary Research, 2003.
6. A. Khalafallah and M. Abdel-Raheem, “Electimize: new evolutionary algorithm for
optimization with application in construction engineering,” Journal of Computing in
Civil Engineering, vol. 25, no. 3, pp. 192–201, 2011.
7. M. Rezania, A. A. Javadi, and O. Giustolisi, “An evolutionary-based data mining
technique for assessment of civil engineering systems,” Engineering Computations
(Swansea, Wales), vol. 25, no. 6, pp. 500–517, 2008.
8. S. Sharma and A. Das, “Backcalculation of pavement layer moduli from falling
weight deflectometer data using an artificial neural network,” Canadian Journal of
Civil Engineering, vol. 35, no. 1, pp. 57–66, 2008
9. R. Bendaña, A. Del Caño, and M. P. De La Cruz, “Contractor selection: Fuzzy-
control approach,” Canadian Journal of Civil Engineering, vol. 35, no. 5, pp. 473–
486, 2008.
10. I. Flood, “Towards the next generation of artificial neural networks for civil
engineering,” Advanced Engineering Informatics, vol. 22, no. 1, pp. 4–14, 2008.
11. S. Laflamme and J. J. Connor, “Application of self-tuning Gaussian networks for
control of civil structures equipped with magnetorheological dampers,” in Active
and Passive Smart Structures and Integrated Systems 2009, vol. 7288
of Proceedings of the SPIE, The International Society for Optical Engineering,
March 2009.

12. J. Li, U. Dackermann, Y. L. Xu, and B. Samali, “Damage identification in civil
engineering structures utilizing PCA-compressed residual frequency response
functions and neural network ensembles,” Structural Control and Health
Monitoring, vol. 18, no. 2, pp. 207–226, 2011.

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