Artificial intelligence in civil engineering seminar report

Artificial Intelligence In Civil Engineering 2020-21
Department of Civil Engineering, KIT, Mangalore 1
CHAPTER 1
INTRODUCTION
1.1 General
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.
In the field of civil engineering it covers a vast area for human benefits especially
in engineering design construction management and program decision-making and can
solve complex problems to the level of experts by imitating the experts. The traditional
methods for design, modeling, optimizing complex structure systems and manual
observation of activities are difficult, time-consuming and prone to error, so, AI helps in
automated data collection and data analysis techniques to improve several aspects of
construction engineering and management for productivity assessment, safety
management, idle time reduction, prediction, risk analysis, decision-making and
optimizing construction costs

CHAPTER 2
DEVELOPMENT OF ARTIFICIAL INTELLIGENCE
2.1 General
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.

CHAPTER 3
INTELLIGENT OPTIMIZATION METHODS IN
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 Evalutionary 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
3.1.2 Artificial Immune System: 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.

3.1.3 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,
togenerate 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 Evalutionary 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.

CHAPTER 4
APPLICATIONS 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.
4.1 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.
4.2 Self Repair Material
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.
4.3 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.
4.4 Estimation
Artificial neural networks(ANN’s) are mostly suited for developing decision aids with
analogy-based problem solving capabilities in estimation.

4.5 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.
4.6 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.
4.7 ANN Or 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 thephone at the wrong
time, coffee breaks). But there is a need for both innovation and governance going
forward for the effective OSHA.
4.8 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.
4.9 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

isessential for the civil engineers in the design procedure. Artificial intelligence
immensely helps in the design of structures to safeguard against the earthquakes.
4.9Neuroform-Neutral Network System For Vertical
Fromwork Section
Fig 4.1 Neural network system
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 therecommendations of

Neuroform when partial inputs are given, demonstrates the system’s fault-tolerant and
generalization properties.
4.10 Belief Networks for Construction Performance
Diagnostics
Fig 4.2 Belief network
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

4.11PavementMaintenance
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.
4.11 Modelling Initial Design Process Ussing Artificial Neural
Network
Fig 4.3 Initial design process
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.

4.12 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 4.4 Intelligent planning

4.13 Construction Fleet Management
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 4.5 Construction Robot Fleet Management System
4.14 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

neededand the complicated interaction between many factors. Considering thistask 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.

Artificial Intelligence In Civil Engineering
Department of Civil Engineering, KIT, Mangalore
5.1Applicability of Artificial Neural Networks to Predict
Mechanicaland Permeability Properties of Volcanic Scoria
Based Concrete
Done By: H. Ceylan and T. Ozcan
H. Ceylan and T. Ozcan presented a case study on theoptimization of headways and
departure times in urban bus
optimization method to evaluate the user and op
solutions in terms of theuser and operator benefits. At the end of the study, theauthors
concluded that total travel time and total service kmcould be reduced by 4.8% and 9.8%,
respectively, comparedwith the current b
Fig 5.1
CHAPTER
CASE TUDIES
Applicability of Artificial Neural Networks to Predict
Mechanicaland Permeability Properties of Volcanic Scoria
Done By: H. Ceylan and T. Ozcan
departure times in urban busnetworks.theauthors used the metaheuristic harmonysearch
optimization method to evaluate the user and operator costs. thiis study gives Pareto
respectively, comparedwith the current bus network.
Fig 5.1 Layout of the studied bus network.
2020-21
CHAPTER 5
Mechanicaland Permeability Properties of Volcanic Scoria-
authors used the metaheuristic harmonysearch
is study gives Pareto

5.2A Computer-Aided Approach to Pozzolanic Concrete Mix
Design
Done by: Ching-Yun Kao , Chin
Shih-Lin Hung
C.-Y. Kao et al. develops a two
mix design. the first step isestablishing a dataset of pozzolanic concrete mixture
proportioningwhich conforms to American Concrete InstituteCode. In the first step,
ANNs are employed to establish theprediction models of co
slump ofthe concrete. Sensitivity analysis of the ANN is used toevaluate the effect of
inputs on the output of the ANN.
experimentalspecimens made in a laboratory for twelve different m
step is classifying the dataset of pozzolanicconcrete mixture proportioning. A
classification method isutilized to categorize the dataset into 360 classes based
oncompressive strength, pozzolanic admixture replacementrate, and material
one can easily obtain mixsolutions based on these factors.
theproposed computer-aided approach is convenient for pozzolanicconcrete mix design
and practical for engineeringapplications.
Fig 5.2
Aided Approach to Pozzolanic Concrete Mix
Yun Kao , Chin-Hung Shen, Jing-Chi Jan, and
Y. Kao et al. develops a two-step computer-aidedapproach for pozzolanic concrete
first step isestablishing a dataset of pozzolanic concrete mixture
ANNs are employed to establish theprediction models of compressive strength and the
inputs on the output of the ANN. the two ANN models are tested using data of
experimentalspecimens made in a laboratory for twelve different mixtures.
oncompressive strength, pozzolanic admixture replacementrate, and material
one can easily obtain mixsolutions based on these factors. the results show that
aided approach is convenient for pozzolanicconcrete mix design
and practical for engineeringapplications.
Fig 5.2 -flow chart of ACI mix design method
2020-21
Aided Approach to Pozzolanic Concrete Mix
Chi Jan, and
approach for pozzolanic concrete
first step isestablishing a dataset of pozzolanic concrete mixture
mpressive strength and the
two ANN models are tested using data of
ixtures. the second
oncompressive strength, pozzolanic admixture replacementrate, and material cost. "us,
results show that
aided approach is convenient for pozzolanicconcrete mix design

5.3Applicability of Artificial Neural Networks to Predict
Mechanical and Permeability Properties of Volcanic Scoria
Based Concrete
Done by:Aref M. al-Swaidani and Waed T. Khwies
A. M. al-Swaidani and W. T. Khwies applied the ANNand
models to estimate 2, 7,28, 90, and 180 days compressive strength, water
permeability,and porosity of concretes containing volcanic scoriaas cement replacement.
Cement content, volcanic scoriacontent, water content, superplasti
curingtime were used as model inputs. "e data used in the ANNmodels were divided into
70% training, 15% testing, and15% validation pattern, respectively. Sensitivity
analysisshowed that all parameters used as an input in this studyhave s
on the properties of concrete containingvolcanic scoria as cement replacement. "e
resultsshowed that ANN models were much more accurate thanMLR models and that
ANN can be used successfully topredict the investigated concrete properties.
fig 5.3 Macrograph of (a) the investigated volcanic scoria and (b) the EDX analysis.
Mechanical and Permeability Properties of Volcanic Scoria
Swaidani and Waed T. Khwies
Swaidani and W. T. Khwies applied the ANNand multilinear regression (MLR)
Cement content, volcanic scoriacontent, water content, superplasticizer content, and
analysisshowed that all parameters used as an input in this studyhave significant effects
Macrograph of (a) the investigated volcanic scoria and (b) the EDX analysis.
2020-21
Mechanical and Permeability Properties of Volcanic Scoria-
multilinear regression (MLR)
cizer content, and
ignificant effects
Macrograph of (a) the investigated volcanic scoria and (b) the EDX analysis.

CHAPTER 6
FUTURE TRENDS
 Fuzzy processing, integrated intelligent technology, intelligent emotion
technology in the civil engineering.
 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.
 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.
 Hybrid intelligence system and a large civil expert system research.
 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.
 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.

CHAPTER 7
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.

CHAPTER 8
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.

CHAPTER 9
CONCLUSION
 The artificial intelligence in civil engineering plays a major role in constructing,
maintaining and managing different aspects of civil engineering problems.
 AI has shown its potency to perform better than the conventional methods.
 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.
 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
 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.
 Artificial Intelligence has been successfully applied to many civil engineering
areas like prediction, risk analysis, decision-making, resources optimization,
classification and selection etc.

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
[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] 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
.

Artificial intelligence in civil engineering seminar report

More Related Content

What's hot (20)

Similar to Artificial intelligence in civil engineering seminar report (20)

Recently uploaded (20)

Artificial intelligence in civil engineering seminar report