Table Of ContentEmerging Trends in Mechatronics
Ehsan Momeni
Danial Jahed Armaghani
Aydin Azizi Editors
Artificial Intelligence
in Mechatronics and
Civil Engineering
Bridging the Gap
https://avxhm.se/blogs/hill0
Emerging Trends in Mechatronics
Series Editor
Aydin Azizi, Oxford, UK
https://avxhm.se/blogs/hill0
· ·
Ehsan Momeni Danial Jahed Armaghani
Aydin Azizi
Editors
Artificial Intelligence
in Mechatronics and Civil
Engineering
Bridging the Gap
https://avxhm.se/blogs/hill0
Editors
Ehsan Momeni Danial Jahed Armaghani
Department of Civil Engineering Civil and Environmental Engineering
Faculty of Engineering University of Technology Sydney
Lorestan University Sydney, NSW, Australia
Khorramabad, Iran
Aydin Azizi
School of Engineering, Computing
and Mathematics
Oxford Brookes University
Oxford, UK
ISSN 2731-4855 ISSN 2731-4863 (electronic)
Emerging Trends in Mechatronics
ISBN 978-981-19-8789-2 ISBN 978-981-19-8790-8 (eBook)
https://doi.org/10.1007/978-981-19-8790-8
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
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About This Book
This book aimed to cover the application of artificial intelligence and simulation
techniques in Engineering. The book highlights the successful implementation of
different soft computing techniques in various areas of engineering more especially
in Civil, Electronic, Mechatronic, and Mining Engineering. This book comprises nine
chapters which, in overall, shed lights on the importance of simulation techniques in
solving complex engineering problems.
Chapter “Optical Resistance Switch for Optical Sensing” deals with optical resis-
tance switch for optical sensing. As regards optical switches can be suitable candi-
dates for optical sensing applications. This chapter focuses on optical switch devices
for optical sensing applications. The optical structures studied are photonic crystal,
plasmonic, and graphene structures which are good platforms for the realization
of optical devices such as optical switches and sensors. Photonic crystals can be
considered as an efficient basis for the realization of optical switches and sensors.
It is because photonic band-gaps with sharp transient edges which are suitable for
switching and sensing mechanisms can be generated at their transmission spectra.
Surface plasmon polaritons have drawn extensive attention in recent decades due to
their ability to break the traditional diffraction limit of light. Graphene structures have
remarkable advantages such as ultra-low ohmic losses, thin thickness, tunability of
complex conductivity, and mechanical strength. In this chapter, relying on theoret-
ical models and numerical simulations, it is shown that optical sensing mechanism
can be achieved by using optical devices such as graphene switches, nanomate-
rial heterostructures (plasmonic and PC structures)-based switch, and plasmonic
amplitude modulators.
Chapter “Empirical, Statistical, and Machine Learning Techniques for Predicting
Surface Settlement Induced by Tunnelling” deals with the application of simulation
techniques in assessing the tunnelling-induced settlement in urban areas. Tunnels
have been constructed in many countries around the world for different purposes, such
as the metro system to mitigate traffic congestion. Since the construction of urban
tunnels is typically conducted at shallow depths, specific concerns such as struc-
tural damage inevitably arise. Surface settlement induced by tunnelling is one of the
common problems encountered during and after tunnelling construction. Therefore,
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vi AboutThisBook
accuracy in the prediction of surface settlement induced by tunnelling is important
to prevent structural damage. Several methods have been previously proposed to
compute tunnelling-induced surface settlement, such as empirical, numerical, labo-
ratory, statistical, and machine learning. Each of these models has advantages and
disadvantages. This study deeply investigates the available techniques to estimate
settlement induced by tunnelling and reviews the most important findings and solu-
tions. Among the existing techniques, machine learning seems to be the most suit-
able technique in estimating settlement induced by tunnelling. These techniques,
with their behind calculations and assumptions, are able to identify the best relations
between independent and dependent parameters and, therefore, to solve complex and
non-linear problems. The discussion provided in this chapter can be advantageous
to those who are interested to conduct research or design in the same field.
Chapter “A Review on the Feasibility of Artificial Intelligence in Mechatronics”
deals with a review on the application of artificial intelligence in mechatronic. Arti-
ficial intelligence has become a valuable tool in various fields with the increasing
progress of science and information production. With the processing of big data and
increased productivity, new challenges have appeared in the design and application
of control systems. In some systems, the interaction between humans and robots
is essential, while real-time decision-making plays a vital role in other types of
systems. This chapter presents a review of artificial intelligence methods in mecha-
tronics. For this purpose, the leading intelligent control methods in technical systems
are reviewed and discussed in the initial part of this chapter, including reinforcement
learning, fuzzy logic method, artificial neural networks, optimization techniques,
and adaptive control methods. The rest of the chapter reviews the applications of
intelligent approaches in engineering control problems. This part is categorized into
five subsections: iterative learning, parametric optimization, identification, controller
tuning, and control problems as stabilization. Finally, in the conclusion of the chapter,
the main challenges in improvements of intelligent control methods are listed.
Chapter “Feasibility of Artificial Intelligence Techniques in Rock Characteri-
zation” deals with the feasibility of soft computing and simulation techniques in
assessing the rock engineering properties. This chapter reviews the recent works
which highlight the workability of the aforementioned techniques in predicting
unconfined compressive strength of rock samples. Based on the provided discus-
sion in this chapter, the artificial intelligence-based predictive models are quick,
economic, and feasible tools in rock characterization.
Chapter “A Review on the Application of Soft Computing Techniques in Founda-
tion Engineering” reviews the application of artificial intelligence methods in founda-
tion engineering. This chapter shed light on many studies which underline the feasi-
bility of simulation-based techniques in assessing the bearing capacity and settlement
of various types of foundation including shallow, deep, and skirted foundations.
Chapter “Application of a Data Augmentation Technique on Blast-Induced
Fly-Rock Distance Prediction” deals with the application of a data augmentation
technique on blast-induced fly-rock distance prediction. Fly-rock induced by blasting
is an inevitable phenomenon in quarry mining, which can give rise to severe hazards,
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AboutThisBook vii
for example, causing damage to buildings and human life. Thus, successful esti-
mation of fly-rock distance is crucial. In this regard, many researchers attempt to
develop empirical, statistical, or machine learning models to accurately predict fly-
rock distance. However, for most previous research, a worrying drawback is that
the amount of data related to fly-rock distance prediction is insufficient because
the measurement work of fly-rock distance is costly for manpower and material
resources. In Chapter six, to deal with the problem of data shortage, authors first
separated the original dataset which was collected from four granite quarry sites in
Malaysia into two parts, i.e., the training and testing sets, and then adopted a data
augmentation technique termed tabular variational autoencoder (TVAE) to augment
the amount of the training (true) data, so as to generate a fresh synthetic data set.
Subsequently, we utilized several statistical methods or plots, such as the boxplot
method, kernel density estimation, cumulative distribution function, and heatmap
method, to testify to the effectiveness of the synthetic data generated by the TVAE
model. Lastly, several commonly used machine learning models were developed to
verify whether the mixed data set which is obtained by merging the training and
synthetic data sets can benefit from the addition of the synthetic data. The work of
verification is implemented on the testing data set. The results demonstrate that the
size of the training data set has increased from the initial 131 to 1000 to obtain a
synthetic data set, and the results of statistical methods proved that the synthetic
data set not only preserves the inner characteristics of the training data set but also
generalizes more diversities compared to the training data set. Further, by comparing
the performance of five machine learning model on three data sets (i.e., the training,
synthetic, and mixed data sets), it can be concluded that the overall performance of
all machine learning models on the mixed data set outperforms that on the training
and synthetic data sets. Consequently, it can be asserted that the application of the
data augmentation technique on the fly-rock distance issue is fruitful in the present
study and has profound engineering application value.
Chapter “Forecast of Modern Concrete Properties Using Machine Learning
Methods” reviews machine learning methods that have been used in the previous
researches to forecast concrete properties. Major classifications and main steps
of machine learning techniques are introduced then their application in concrete
science is discussed. Artificial neural network, fuzzy logic, decision tree, support
vector machine, gene expression programing, bagging, and boosting are the most
commonly machine learning methods. A typical machine learning-based studies
comprise problem description, data collection, data pre-processing, model devel-
opment, and model assessment. Concretes noticed in this review include ordinary,
self-consolidation, ultra-high-performance, alkali-activated, and recycled aggregate
types. Inputs, prediction methods, and outputs for each concrete type is reviewed
in different researches and summarized as review result. The chapter illustrates
that machine learning techniques are capable of predicting a wide range of proper-
ties, including mechanical properties, freshness properties, and durability properties.
According to this chapter, concrete compressive strength is the most frequent prop-
erty and artificial neural network is the most frequently machine learning method that
different researchers dealt with. Activation functions, network architecture, learning
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viii AboutThisBook
rules, and hybrid models of artificial neural network are discussed in details. In
general, this chapter suggests that machine learning methods perform better than
classical regression models.
Chapter “Reliability-Based Design Optimization of Detention Rockfill Dams
and Investigation of the Effect of Uncertainty on Their Performance Using
Meta-Heuristic Algorithm” deals with Reliability-based design optimization of
detention rockfill dams. Detention rockfill dams are one of the most popular struc-
tures which are used for reducing the flood peak and also increasing the downstream
response time against the occurrence of floods. Designing such structures is a crit-
ical issue because of the complex interaction between the coarse porous rockfill
material and the flow, the non-Darcian flow in rockfills, their stable encounter with
floods, reliable flood peak reduction, and safe flood discharge release downstream.
In this chapter, the preliminary design is performed using a simulation model in
order to obtain the initial height and length of the dam and then using a simulation-
optimization model, the optimization of the dams is done to provide structural safety
factors. Also, both preliminary and optimal designs are investigated for their reli-
abilities under uncertain conditions. Using the Monte Carlo simulation method as
well as the simulation and optimization models, the effects of uncertainty in design
parameters are investigated. In this step, LHS is used for generating samples, and
the rejection rule is implied for deleting some samples which are greater than 15%
uncertainty. The uncertainty of input parameters in model design consideration is
investigated including the uncertainties of inlet hydrograph, volume-elevation rela-
tion of the reservoir, non-Darcian stage-discharge equation in coarse rockfill material,
and non-linear flood routing in detention rockfill dams. At the end of this chapter,
a reliability-based design optimization (RBDO) of the detention rockfill dam was
carried out using self-adaptive NSGA-||. The first objective function in this multi-
objective algorithm is the minimization of the dam cost and the second one is the
maximization of the reliability function.
Chapter “Machine Learning in Mechatronics and Robotics and Its Application
in Face-Related Projects” deals with various aspects of face-related projects. Imple-
menting non-verbal information is one of the ways to create communication between
people. Using this type of information can improve the interaction process in human–
robot interaction. One of the aspects of people’s non-verbal information is facial
images, which can play an essential role in the development of mechatronic and
robotic systems. This chapter discusses some uses of facial images, such as facial
recognition, and facial expression recognition. Using these images and applica-
tions, authors explored and developed mechatronic and robotic systems which were
based on special access for different persons and changes in facial expressions.
As mentioned earlier, in chapter nine, different aspects of face-related projects are
explored, apart from that, some ideas that can be applied to create new approaches
are discussed. Since these projects perform based on a procedure, the flow of the
facial projects from face detection to facial expression recognition and other appli-
cations are discussed. This chapter helps the readers get acquainted with using facial
images in human–robot interaction. Furthermore, enough information is provided
to understand the process of the methods used before, from traditional to modern
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AboutThisBook ix
approaches, and to know the process of developing new methods. In the different
parts, the datasets related to each application are introduced to have a complete
view of facial image-based projects. By understanding these applications and how
to create innovative methods, the way to use facial images is paved to provide new
face image-based systems.
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Contents
Optical Resistance Switch for Optical Sensing ........................ 1
Shiva Khani, Ali Farmani, and Pejman Rezaei
Empirical, Statistical, and Machine Learning Techniques
for Predicting Surface Settlement Induced by Tunnelling .............. 39
Chia Yu Huat, Danial Jahed Armaghani, Ehsan Momeni, and Sai Hin Lai
A Review on the Feasibility of Artificial Intelligence in Mechatronics ... 79
Amin Hashemi and Mohammad Bagher Dowlatshahi
Feasibility of Artificial Intelligence Techniques in Rock
Characterization .................................................. 93
Mohamad Bagher Dowlatshahi, Amin Hashemi, Masoud Samaei,
and Ehsan Momeni
A Review on the Application of Soft Computing Techniques
in Foundation Engineering ......................................... 111
Ehsan Momeni, Masoud Samaei, Amin Hashemi,
and Mohamad Bagher Dowlatshahi
Application of a Data Augmentation Technique on Blast-Induced
Fly-Rock Distance Prediction ....................................... 135
Biao He, Danial Jahed Armaghani, and Sai Hin Lai
Forecast of Modern Concrete Properties Using Machine Learning
Methods .......................................................... 167
Yashar Asghari, Golnaz Sadeghian,
Seyed Esmaeil Mohammadyan-Yasouj, and Elahe Mirzaei
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