Chapters authored and co-authored in scientific books
and reference volumes.
BC12. Secant Method
Chapter authors:
V. Plevris and A. Almutairi
Chapter number:
19
Chapter pages:
1-7
Book title:
Encyclopedia of Engineering Optimization and Heuristics
Book editors:
A. J. Kulkarni, E. Mezura-Montes and H. Bonakdari
Book publisher:
Springer
E-ISBN-13: 978-981-96-8165-5
Book publication:
2026
The secant method in single-variable optimization is a first-order iterative procedure that locates stationary points of a differentiable function without requiring explicit second-derivative information. Instead of using the exact curvature, as in Newton’s method, it approximates the second derivative by a finite difference of successive first derivatives. Each iteration updates the estimate by effectively minimizing a locally linearized model of the derivative function, yielding superlinear convergence near a nondegenerate stationary point. As a second-derivative–free alternative to Newton’s method, it is especially useful when evaluating or computing the second derivative is difficult or costly, while still offering faster convergence than basic gradient-based methods. The method requires two initial guesses.
BC11. Newton’s Method
Chapter authors:
A. Almutairi and V. Plevris
Chapter number:
17
Chapter pages:
1-7
Book title:
Encyclopedia of Engineering Optimization and Heuristics
Book editors:
A. J. Kulkarni, E. Mezura-Montes and H. Bonakdari
Book publisher:
Springer
E-ISBN-13: 978-981-96-8165-5
Book publication:
2026
Newton’s method in single-variable optimization is a second-order iterative procedure that locates stationary points of a differentiable function using both the first and second derivatives. Each iteration updates the estimate by minimizing a local quadratic model of the function, enabling fast convergence under appropriate smoothness conditions. The approach naturally generalizes to multivariable optimization, where the Hessian matrix replaces the scalar second derivative.
BC10. Blockchain applications in the construction industry
Chapter authors:
V. Plevris
Chapter number:
12
Chapter pages:
265-290
Book title:
Digital Twin and Blockchain for Sensor Networks in Smart Cities
Book editors:
T.A. Nguyen
Book publisher:
Elsevier
ISBN-13 (Softcover): 9780443300769, E-ISBN-13: 9780443300776
Book publication:
April
2025
(First online: 28 March 2025)
This chapter delves into the transformative potential of blockchain technology in the construction industry, a sector pivotal to the global economy yet plagued by inefficiencies, lack of transparency, and slow adoption of digital innovations. It presents a comprehensive analysis, starting from the fundamental concepts of blockchain technology, including its decentralized nature, security features, and the role of consensus mechanisms like Proof of Work and Proof of Stake. The exploration extends to the practical applications of blockchain across various aspects of construction management, such as supply chain management, contract management, project management and documentation, and real estate transactions. Highlighting the sector’s critical challenges, including fragmented communication, inefficient supply chain management, and the perennial issues of delays and budget overruns, the chapter positions blockchain as a potent solution capable of enhancing transparency, efficiency, and accountability within the industry. It provides detailed insights into how blockchain can streamline operations, from automating procurement and improving inventory management to securing land registry processes and facilitating real-time tracking of materials. Moreover, the study reviews significant scholarly contributions to the field, offering a bibliometric analysis that underscores the burgeoning interest and research activity surrounding blockchain in construction and engineering. It also discusses the technical hurdles, legal and regulatory considerations, and the financial implications of blockchain adoption, laying out a balanced view of the opportunities and obstacles ahead. Concluding with a forward-looking perspective, the chapter envisions a future where blockchain technology not only addresses the current limitations of the construction industry but also fosters a new era of innovation, sustainability, and collaboration. Through this comprehensive examination, the chapter contributes valuable knowledge to academics, industry professionals, and policymakers interested in the intersection of blockchain technology and construction, encouraging further exploration and adoption of this groundbreaking technology in shaping the future of the built environment.
BC09. Estimating the In-Plane Lateral Resistance of Reinforced Log Wall Employing Soft Modelling Techniques
Chapter authors:
K.G.M. Kandethanthri, M. Nikoo, G. Hafeez, A. Bagchi and V. Plevris
Chapter number:
1
Chapter pages:
1-21
Book title:
Advanced Optimization Applications in Engineering
Book editors:
A. Ahmad and C.V. Camp
Book publisher:
IGI Global
ISBN-13 (Hardcover): 9798369321614, ISBN-13 (Softcover): 9798369344576, E-ISBN-13: 9798369321621
Book publication:
May
2024
The popularity of log houses has been on the rise in numerous regions worldwide. In the context of log construction, the stability of log walls is notably influenced by the friction existing between the layers of logs and the openings designated for windows and doors. This study endeavors to comprehensively evaluate the lateral resistance of log walls through an extensive parametric analysis utilizing finite element (FE) methods. To construct a robust dataset, a total of 71 distinct samples were generated employing FE analysis, where the shuffled frog-leaping algorithm (SFLA) was incorporated in conjunction with a feed-forward (FF) neural network. Within this framework, the accuracy of the SFLA-based informational model was juxtaposed against that of an artificial neural network (ANN) coupled with particle swarm optimization (PSO), genetic algorithm (GA), and statistical models including multiple linear regression (MLR).
BC08. Overview on Machine Learning Assisted Topology Optimization Methodologies
Chapter authors:
I. Chamatidis, M. Stoumpos, G. Kazakis, N.A. Kallioras, S. Triantafyllou, V. Plevris and N.D. Lagaros
Chapter number:
10
Chapter pages:
373-394
Book title:
Machine Learning in Modeling and Simulation
Book editors:
T. Rabczuk and K-J. Bathe
Book publisher:
Springer Nature
Series:
Computational Methods in Engineering & the Sciences (CMES)
ISBN-13 (Hardcover): 978-3-031-36643-7, ISBN-13 (Softcover): 978-3-031-36646-8, E-ISBN-13: 978-3-031-36644-4
Book publication:
October
2023
The past two decades saw tremendous developments in artificial intelligence (AI). Advancements in software, algorithms, and hardware led to the development of significantly more accurate and versatile artificial intelligence models. This rendered artificial intelligence a powerful tool that is used in diverse scientific areas, e.g. medicine and drug design, economics, and self-driving cars, among many others. These methods, having been successfully implemented in the simulation and modeling of structures, found their way to topology optimization problems, where artificial intelligence appears to have great potential for successful implementation. In conventional topology optimization, the optimal design of a specific domain must be calculated subject to specific constraints and the objective is to minimize the total compliance of the structure and use a specific amount of material. This is typically an iterative process that involves large matrices and can be very timeconsuming. By means of artificial intelligence models, referred to also as surrogate models (or surrogates), the computing time can be reduced significantly. The surrogate model is apriori trained offline. Following, during the optimization process the model is inferred based on input data, which is a lot faster due to limited matrix multiplications that the surrogate performs. The usual process involves either an artificial intelligence surrogate that complements the conventional procedure to reduce computational costs or a standalone surrogate which calculates the whole optimized structures by itself. The AI surrogates that are used belong to two main categories, i.e. Surrogates that use density and surrogates that use images. The surrogates that use density have similar inputs as the conventional method since the optimization process uses the density of the structure and is updated in each iteration of the AImodel. The surrogates that perform optimization on images are a bit different because they use techniques like image segmentation and filtering to output the optimized image (structure) which then is mapped into density. Most surrogates can be used for 2D and 3D structures and they are transferable, meaning that once trained they can be used in another topology optimization problem (thermodynamics or different material). The Background section contains an introduction to artificial intelligence, the surrogate models that will be used and an introduction to conventional topology optimization. The Literature Survey section provides areview ofrecent advancements of topology optimization using artificial intelligence models. This section is divided into two parts, the first describing the models that use density and the second the models that use image-based approaches.
BC07. Predicting the Fundamental Period of Light-Frame Wooden Buildings by Employing Bat Algorithm-Based Artificial Neural Network
Chapter authors:
M. Nikoo, G. Hafeez, G. Doudak and V. Plevris
Chapter number:
6
Chapter pages:
139-162
Book title:
Artificial Intelligence and Machine Learning Techniques for Civil Engineering
Book editors:
V. Plevris, A. Ahmad and N.D. Lagaros
Book publisher:
IGI Global
ISBN-13 (Hardcover): 9781668456439, ISBN-13 (Softcover): 9781668474303, E-ISBN-13: 9781668456446
Book publication:
June
2023
The study utilizes an artificial neural network model for determining the fundamental period of Light-Frame Wooden Buildings, employing the Bat algorithm on a data set of 71 measured periods of wooden buildings. The number of stories, floor area, storey height, maximum length, and maximum width are selected as input parameters to estimate the fundamental period of light-frame wooden buildings. The accuracy and the competitiveness of the developed model were evaluated by comparing it with a similar particle swarm optimization (PSO)- ANN scheme, the formulas provided in the National Building Code of Canada, an equation obtained from the Eureqa software, and a non-linear regression (NLR) model. The results of the research show that the bat-ANN model exhibited the best overall performance with the lowest RMSE and MAE error values and the highest values of the Coefficient of determination, R2, in comparison to the other examined models. Therefore, the proposed Bat-ANN model can be considered as a reliable, robust, and accurate tool for predicting the fundamental period of wooden buildings.
BC06. Design of RC Sections with Single Reinforcement According to EC2-1-1 and the Rectangular Stress Distribution
Chapter authors:
V. Plevris and G. Papazafeiropoulos
Chapter number:
8
Chapter pages:
205-259
Book title:
Computational Methods in Earthquake Engineering, Vol. 3
Book editors:
M. Papadrakakis, V. Plevris and N.D. Lagaros
Book publisher:
Springer International Publishing
Series:
Computational Methods in Applied Sciences
(Vol. 44)
ISBN-13 (Hardcover): 978-3-319-47796-1, ISBN-13 (Softcover): 978-3-319-83827-4, E-ISBN-13: 978-3-319-47798-5
Book publication:
2017
(First online: 23 December 2016)
Nowadays, the design of concrete structures in Europe is governed by the application of Eurocode 2 (EC2). In particular, EC2—Part 1-1 deals with the general rules and the rules for concrete buildings. An important aspect of the design is specifying the necessary tensile (and compressive, if needed) steel reinforcement required for a Reinforced Concrete (RC) section. In this study we take into account the equivalent rectangular stress distribution for concrete and the bilinear stress-strain relation with a horizontal top branch for steel. This chapter presents three detailed methodologies for the design of rectangular cross sections with tensile reinforcement, covering all concrete classes, from C12/15 up to C90/105. The purpose of the design is to calculate the necessary tensile steel reinforcement. The first methodology provides analytic formulas and an algorithmic procedure that can be easily implemented in any programming language. The second methodology is based on design tables that are provided in Appendix A, requiring less calculations. The third methodology provides again analytic formulas that can replace the use of tables and even be used to reproduce the design tables. Apart from the direct problem, the inverse problem is also addressed, where the steel reinforcement is given and the purpose is to find the maximum bending moment that the section can withstand, given also the value and position of the axial force. For each case analytic relations are extracted in detail with a step-by-step procedure, the relevant assumptions are highlighted and results for four different cross section design examples are presented.
BC05. Numerical Modeling of Historic Masonry Structures
Chapter authors:
P. G. Asteris, V. Sarhosis, A. Mohebkhah, V. Plevris, L. Papaloizou, P. Komodromos and J. V. Lemos
Chapter number:
7
Chapter pages:
213-256
Book title:
Handbook of Research on Seismic Assessment and Rehabilitation of Historic Structures
Book editors:
P. G. Asteris and V. Plevris
Book publisher:
IGI Global
ISBN-13 (Hardcover): 9781466682863, E-ISBN-13: 9781466682870
Book publication:
July
2015
The majority of historical and heritage structures around the world consist of unreinforced masonry walls. A masonry structure is composed of masonry units, such as brick or marble blocks, with or without a joint filling material, such as mortar. A masonry with a joint material is usually made of two different materials (i.e. masonry units and mortar), representing a non-homogeneous and anisotropic structural component. In other words, masonry is a discontinuous structural component whose deformations and failure mechanism are governed by its blocky behavior. Some ancient masonry structures, such as ancient columns and colonnades, are constructed without any form of joint material between the individual blocks. Therefore, the isotropic elastic continuum-based models are not suitable for the simulation of the real nonlinear behavior of masonry walls under applied load.
BC04. Metaheuristic Optimization in Seismic Structural Design and Inspection Scheduling of Buildings
Chapter authors:
Ch.Ch. Mitropoulou, V. Plevris and N.D. Lagaros
Chapter number:
7
Chapter pages:
148-169
Book title:
Structural Seismic Design Optimization and Earthquake Engineering: Formulations and Applications
Book editors:
V. Plevris, Ch.Ch. Mitropoulou and N.D. Lagaros
Book publisher:
IGI Global
ISBN-13 (Hardcover): 978-1-4666-1640-0, E-ISBN-13: 978-1-4666-1641-7
Book publication:
May
2012
Optimization is a field where extensive research has been conducted over the last decades. Many types of problems have been addressed and many types of algorithms have been developed, while their range of applications is continuously growing. The chapter is divided into two parts; in the first part, the life-cycle cost analysis is used as an assessment tool for designs obtained by means of prescriptive and performance-based optimum design methodologies. The prescriptive designs are obtained through a single-objective formulation, where the initial construction cost is the objective to be minimized, while the performance-based designs are obtained through a two-objective formulation where the life-cycle cost is considered as an additional objective also to be minimized. In the second part of the chapter, the problem of inspection of structures and routing of the inspection crews following an earthquake in densely populated metropolitan areas is studied. A model is proposed and a decision support system is developed to aid local authorities in optimally assigning inspectors to critical infrastructures. A combined particle swarm – ant colony optimization based framework is implemented which proves to be an instance of a successful application of the philosophy of bounded rationality and decentralized decision-making for solving global optimization problems.
BC03. A Swarm Intelligence Approach For Emergency Infrastructure Inspection Scheduling
Chapter authors:
V. Plevris, M.G. Karlaftis and N.D. Lagaros
Chapter number:
8
Chapter pages:
201-230
Book title:
Sustainable and Resilient Critical Infrastructure Systems: Simulation, Modeling, and Intelligent Engineering
Book editors:
K. Gopalakrishnan and S. Peeta
Book publisher:
Springer
ISBN-13 (Hardcover): 978-3-642-11404-5
Book publication:
2010
Natural hazards such as earthquakes, floods and tornadoes can cause extensive failure of critical infrastructures including bridges, water and sewer systems, gas and electricity supply systems, and hospital and communication systems. Following a natural hazard, the condition of structures and critical infrastructures must be assessed and damages have to be identified; inspections are therefore necessary since failure to rapidly inspect and subsequently repair infra-structure elements will delay search and rescue operations and relief efforts. The objective of this work is scheduling structure and infrastructure inspection crews following an earthquake in densely populated metropolitan areas. A model is proposed and a decision support system is designed to aid local authorities in optimally assigning inspectors to critical infrastructures. A combined Particle Swarm – Ant Colony Optimization based framework is developed which proves an instance of a successful application of the philosophy of bounded rationality and decentralized decision-making for solving global optimization problems.
BC02. Metamodel-based Computational Techniques for Solving Structural Optimization Problems Considering Uncertainties
Chapter authors:
N.D. Lagaros, Y. Tsompanakis, M. Fragiadakis, V. Plevris and M. Papadrakakis
Chapter number:
21
Chapter pages:
567-597
Book title:
Structural Design Optimization Considering Uncertainties
Book editors:
Y. Tsompanakis, N.D. Lagaros and M. Papadrakakis
Book publisher:
Taylor & Francis
ISBN-13 (Hardcover): 978-0-415-45260-1, E-ISBN-13: 978-0-203-93852-2
Book publication:
2008
Uncertainties are inherent in engineering problems due to various numerical modeling "imperfections" and due to the inevitable scattering of the design parameters from their nominal values. Under this perspective, there are two main optimal design formulations that account for the probabilistic response of structural systems: Reliability-based Design Optimization (RBDO) and Robust Design Optimization (RDO). In this work both type of problems are briefly addressed and realistic engineering applications are presented. The optimization part of the proposed probabilistic formulations is solved utilizing efficient evolutionary methods. In both types of problems the probabilistic analysis is carried out with the Monte Carlo Simulation (MCS) method incorporating the Latin Hypercube Sampling (LHS) technique for the reduction of the sample size. In order to achieve further improvement of the computational efficiency a Neural Network (NN) is used to replace the time-consuming FE analyses required by the MCS. Moreover, various sources of randomness that arise in structural systems are taken into account in a "holistic" probabilistic perception by implementing a Reliability-based Robust Design Optimization (RRDO) formulation, where additional probabilistic constraints are incorporated into the standard RDO formulation. The proposed RRDO problem is formulated as a multi-criteria optimization problem using the non-dominant Cascade Evolutionary Algorithm (CEA) combined with the weighted Tchebycheff metric.
BC01. Multi-objective Optimization of Space Structures under Static and Seismic Loading Conditions
Chapter authors:
N.D. Lagaros, M. Papadrakakis and V. Plevris
Chapter number:
12
Chapter pages:
273-300
Book title:
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications
Book editors:
A. Abraham, L. Jain and R. Goldberg
Book publisher:
Springer
ISBN-13 (Hardcover): 978-1-85233-787-2, ISBN-13 (Softcover): 978-1-84996-916-1, E-ISBN-13: 978-1-84628-137-2
Book publication:
2005
This chapter presents a evolution strategies approach for multiobjective design optimization of structural problems such as space frames and multi-layered space trusses under static and seismic loading conditions. A rigorous approach and a simplified one with respect to the loading condition are implemented for finding optimal design of a structure under multiple objectives.