Peer-reviewed journal articles covering structural engineering, computational mechanics, optimization, artificial intelligence, and related fields.
Research Article
J44. Predicting Crack Width in CFRP Strengthened RC One-Way Slabs Using Hybrid Grey Wolves Optimizer Neural Network Model
Authors:
S.V.R. Tosee, I. Faridmehr, M.L. Nehdi*, V. Plevris and K.A. Valerievich
Journal:
Buildings
Publisher:
MDPI
Volume: 12, Issue: 11
Article ID:
1870
Page count:
26
Year: 2022
(First online: 03 November 2022)
This study deploys a hybrid Grey Wolf Optimizer Neural Network Model for predicting the crack width in reinforced concrete slabs strengthened with carbon fiber-reinforced polymers (CFRP). Reinforced concrete (RC) one-way slabs (1800 × 400 × 120 mm in size) were strengthened with CFRP with various lengths (1800, 1100, and 700 mm) and subjected to four-point bending. The experimental results were compared to corresponding values for conventional RC slabs. The observed crack width results were recorded, and subsequently examined against the expression recommended by Eurocode 2. To estimate the crack width of CFRP-reinforced slabs, ANN combined with the Grey Wolf Optimizer algorithm was employed whereby the applied load, CFRP width/length, X/Y crack positions, and stress in steel reinforcement and concrete were defined as the input parameters. Experimental results showed that the larger the length and width of the carbon fiber, the smaller the maximum crack width in the tensile area of the slab at the final load step. On average, the crack width in slabs retrofitted with CFRP laminates increased by around 80% compared to a slab without CFRP. The results confirm that the equation provided by Eurocode 2 provides an unconservative estimation of crack widths for RC slabs strengthened with CFRP laminates. On the other hand, the results also confirm that the proposed informational model could be used as a reliable tool for estimating the crack width in RC slabs. The findings provide valuable insight into the design approaches for RC slabs and rehabilitation strategies for existing deficient RC slabs using CFRP.
Review Article
J43. Computational Intelligence Methods in Simulation and Modeling of Structures: A State-of-the-art Review using Bibliometric Maps
Authors:
G. Solorzano* and V. Plevris
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 8
Article ID:
1049616
Year: 2022
(First online: 21 October 2022)
The modeling and simulation of structural systems is a task that requires high precision and reliable results to ensure the stability and safety of construction projects of all kinds. For many years now, structural engineers have relied on hard computing strategies for solving engineering problems, such as the application of the Finite Element Method (FEM) for structural analysis. However, despite the great success of FEM, as the complexity and difficulty of modern constructions increases, the numerical procedures required for their appropriated design become much harder to process using traditional methods. Therefore, other alternatives such as Computational Intelligence (CI) techniques are gaining substantial popularity among professionals and researchers in the field. In this study, a data-driven bibliometric analysis is presented with the aim to investigate the current research directions and the applications of CI-based methodologies for the simulation and modeling of structures. The presented study is centered on a self-mined database of nearly 8000 publications from 1990 to 2022 with topics related to the aforementioned field. The database is processed to create various two-dimensional bibliometric maps and analyze the relevant research metrics. From the maps, some of the trending topics and research gaps are identified based on an analysis of the keywords. Similarly, the most contributing authors and their collaborations are assessed through an analysis of the corresponding citations. Finally, based on the discovered research directions, various recent publications are selected from the literature and discussed in detail to set examples of innovative CI-based applications for the modeling and simulation of structures. The full methodology that is used to obtain the data and generate the bibliometric maps is presented in detail as a means to provide a clearer interpretation of the bibliometric analysis results.
Review Article
J42. Bibliometric Literature Review of Adaptive Learning Systems
Authors:
D. Koutsantonis, K. Koutsantonis, N.P. Bakas*, V. Plevris, A. Langousis and S.A. Chatzichristofis*
Journal:
Sustainability
Publisher:
MDPI
Volume: 14, Issue: 19
Article ID:
12684
Page count:
18
Year: 2022
(First online: 05 October 2022)
In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.
Research Article
J41. Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes
Authors:
M.E.A. Ben Seghier, V. Plevris* and G. Solorzano
Journal:
Structures
Publisher:
Elsevier
Volume: 44
Pages:
261–273
Year: 2022
(First online: 09 August 2022)
Despite the existence of methods for estimating the behavior of steel circular tubes subjected to pure bending, analytical models are still restricted due to the problem’s complexity and significant nonlinearity. Using the random forest (RF) as the basic model, novel intelligent models are constructed to estimate the ultimate pure bending capacity of circular steel tubes in this study. The RF model’s parameters are optimized using three nature inspired optimization algorithms, namely, the particle swarm optimization (PSO), ant colony optimization (ACO) and whale optimization algorithm (WOA). In the experimental part, a database of 104 tests that comprise 49 and 55 pure bending tests conducted on fabricated and cold-formed steel circular tubes, respectively, are evaluated and utilized to investigate the applicability of the hybrid RF-models. A single RF model is also built for comparative reasons in order to estimate the ultimate pending capacity. Various statistical and graphical measures are used to evaluate the performance of the developed models. The results show that the proposed RF-based nature-inspired algorithms can outperform the original RF predictive model. When the hybrid-RF models were assessed, it was discovered that the RF-WOA performed best. In addition, the influence of each parameter on the prediction findings based on the best RF-model is investigated via sensitivity analysis. Taking into account the overall findings, the hybrid RF-models may be used as powerful tools to predict the ultimate bending capacity of circular steel tubes and may be viable to aid technicians in making proper judgments.
Editorial
J40. Artificial Intelligence (AI) Applied in Civil Engineering
Authors:
N.D. Lagaros and V. Plevris*
Journal:
Applied Sciences
Publisher:
MDPI
Volume: 12, Issue: 15
Article ID:
7595
Year: 2022
(First online: 28 July 2022)
This editorial introduces the MDPI Topic “Artificial Intelligence (AI) Applied in Civil Engineering”, guest-edited by Nikos D. Lagaros and Vagelis Plevris. The Topic brings together 35 research contributions from researchers across 19 countries, published in the journals Applied Sciences, Mathematics, and Symmetry. It highlights the rapidly growing role of artificial intelligence in civil engineering, bringing together innovative methodologies and practical applications across structural, transportation, geotechnical, hydraulic, and environmental engineering, as well as construction management and structural health monitoring. The collection covers a broad range of AI-driven approaches, including optimization algorithms, machine learning, deep learning, convolutional neural networks, intelligent monitoring, and hybrid computational techniques. The editorial provides an overview of these contributions, emphasizing the potential of AI to improve engineering analysis, prediction, design optimization, and decision-making, while encouraging further research and innovation at the intersection of artificial intelligence and civil engineering.
Review Article
J39. The mosaic of metaheuristic algorithms in structural optimization
Authors:
N.D. Lagaros*, V. Plevris and N.A. Kallioras
Journal:
Archives of Computational Methods in Engineering
Publisher:
Springer Nature
Volume: 29, Issue: 7
Pages:
5457–5492
Year: 2022
(First online: 11 July 2022)
Metaheuristic optimization algorithms (MOAs) represent powerful tools for dealing with multi-modal nonlinear optimization problems. The considerable attention that MOAs have received over the last decade and especially when adopted for dealing with several types of structural optimization problems can be mainly credited to the advances achieved in computer science and computer technology rendering possible, among others, the solution of real-world structural design optimization cases in reasonable computational time. The primal scope of the study is to present a state-of-the-art review of past and current developments achieved so far in structural optimization problems dealt with MOAs, accompanied by a set of tests aiming to examine the efficiency of various MOAs in several benchmark structural optimization problems. For this purpose, 24 population-based state-of-the-art MOAs belonging in four classes, (i) swarm-based; (ii) physics-based; (iii) evolutionary-based; and (iv) human-based, are used for solving 11 single objective benchmark structural optimization test problems of different levels of complexity. The size of the problems employed varies, with the number of unknowns ranging from 3 to 328 and the number of constraint functions ranging from 2 to 264, related to the structural performance of the design with reference to deformation and stress limits.
Research Article
J38. A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking
Authors:
V. Plevris and G. Solorzano*
Journal:
Data
Publisher:
MDPI
Volume: 7, Issue: 4
Article ID:
46
Page count:
52
Year: 2022
(First online: 11 April 2022)
A collection of thirty mathematical functions that can be used for optimization purposes is presented and investigated in detail. The functions are defined in multiple dimensions, for any number of dimensions, and can be used as benchmark functions for unconstrained multidimensional single-objective optimization problems. The functions feature a wide variability in terms of complexity. We investigate the performance of three optimization algorithms on the functions: two metaheuristic algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one mathematical algorithm, Sequential Quadratic Programming (SQP). All implementations are done in MATLAB, with full source code availability. The focus of the study is both on the objective functions, the optimization algorithms used, and their suitability for solving each problem. We use the three optimization methods to investigate the difficulty and complexity of each problem and to determine whether the problem is better suited for a metaheuristic approach or for a mathematical method, which is based on gradients. We also investigate how increasing the dimensionality affects the difficulty of each problem and the performance of the optimizers. There are functions that are extremely difficult to optimize efficiently, especially for higher dimensions. Such examples are the last two new objective functions, F29 and F30, which are very hard to optimize, although the optimum point is clearly visible, at least in the two-dimensional case.
Review Article
J37. Blockchain in Civil Engineering, Architecture and Construction Industry: State of the Art, Evolution, Challenges and Opportunities
Authors:
V. Plevris*, N.D. Lagaros and A. Zeytinci
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 8
Article ID:
840303
Year: 2022
(First online: 29 March 2022)
Blockchain is a technology that allows the recording of information in a way that it is difficult or practically impossible to alter, hack, or cheat. It is a new, promising technology, considered by many as a general-purpose technology (GPT). GPTs are technologies that have the potential to affect an entire economy, impacting economic growth and transforming both everyday life and the ways in which we conduct business. We present a bibliometric analysis of the relevant literature, followed by a discussion about monetary mediums and the evolution of bitcoin, as the first digital medium managing to solve the “double-spending” problem and the first successful implementation of blockchain technology. The computational operations involved in blockchain are presented, together with the cryptographic technologies associated with it, its unique characteristics, and the advantages it offers as a technology. A comprehensive literature review is provided, of the current state of the art in blockchain in the fields of civil engineering, architecture and the construction industry. Six important application areas are identified, and the relevant literature is investigated. Namely, building information modelling and computer aided design, contract management and smart contracts, construction project management, smart buildings and smart cities, construction supply chain management, and real estate. Finally, we discuss the future applications, the challenges and the opportunities that blockchain technology brings to these fields.
Research Article
J36. Novel Hybrid Informational Model for Predicting the Creep and Shrinkage Deflection of Reinforced Concrete Beams Containing GGBFS
Authors:
I. Faridmehr, M. Shariq, V. Plevris* and N. Aalimahmoody
Journal:
Neural Computing and Applications
Publisher:
Springer Nature
Volume: 34, Issue: 15
Pages:
13107–13123
Year: 2022
(First online: 28 March 2022)
This study investigates a Novel Hybrid Informational model for the prediction of creep and shrinkage deflection of reinforced concrete (RC) beams containing different percentages of ground granulated blast furnace slag (GGBFS) at different ages, varying from 1 to 150 days. The percentage of cement replacement by GGBFS varies from 20 to 60%. In order to examine the effects of the applied load and tensile reinforcement on creep behavior, the magnitude of two-point loading was varied from 200 kg to a maximum of 350 kg while the percentage of tensile reinforcement (ρ) was selected as either 0.77% or 1.2%. The current situation about short-term and long-term deflections due to creep and shrinkage available in the international standards, including ACI, BS and Eurocode 2, is discussed. The results indicate that RC beams containing GGBFS have larger deflections than the ones with conventional concrete (i.e., ordinary Portland cement concrete). After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic Whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. Several statistical metrics, including the root mean square error and the coefficient of variation, revealed that the generalized model achieved the most reliable and accurate prediction of the concrete beam’s deflection in comparison with international standards and other models. This novel informational model can simplify the design processes in computational intelligence structural design platforms in future.
Editorial
J35. Editorial: Artificial Intelligence-powered Methodologies and Applications in Earthquake and Structural Engineering
Authors:
X. Lu*, V. Plevris, G. Tsiatas and D. De Domenico
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 8
Article ID:
876077
Year: 2022
(First online: 18 March 2022)
This editorial introduces the Research Topic “Artificial Intelligence-Powered Methodologies and Applications in Earthquake and Structural Engineering”, published in the Earthquake Engineering section of Frontiers in Built Environment. Edited by Xinzheng Lu, Vagelis Plevris, George Tsiatas, and Dario De Domenico, the collection brings together four research contributions exploring the application of artificial intelligence (AI) to earthquake and structural engineering. The editorial highlights the growing importance of machine learning, deep learning, and other AI-based techniques for addressing complex engineering challenges, including seismic response prediction, structural damage assessment, vibration monitoring, and computationally efficient structural analysis. The contributions demonstrate the potential of intelligent data-driven approaches to complement traditional physics-based methods, improve predictive capabilities, and reduce computational demands. The editorial emphasizes the expanding role of AI in advancing earthquake engineering research and developing more effective approaches to structural safety and seismic risk assessment.
Research Article
J34. An Alternative Approach for Measuring the Mechanical Properties of Hybrid Concrete through Image Processing and Machine Learning
Authors:
M.I. Waris, V. Plevris*, J. Mir, N. Chairman and A. Ahmad
Journal:
Construction and Building Materials
Publisher:
Elsevier
Volume: 328
Article ID:
126899
Year: 2022
(First online: 16 March 2022)
Image processing (IP), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are innovative techniques in computer science that have been widely used to predict the properties of materials in structural engineering. The stability of reinforced concrete structures mainly depends on the mechanical properties of concrete, i.e., its compressive strength fc and tensile strength ft. Different kinds of inexpensive cement replacement materials (CRM) can be used to form hybrid concrete (HC) with enhanced mechanical and other properties. In this study, the IP, ANN, and ANFIS methods are properly combined and used to predict the mechanical properties of hybrid concrete. For this, 162 cylindrical specimens of HC with 0%, 15%, and 25% silica fume and fly ash as replacement material with three mix ratios, 1:3:6, 1:2:4, and 1:1.5:3 were cast at 14 days and 28 days curing. The specimens were divided into three equal sets as follows: (i) the first to find the compressive strength, (ii) the second to find the split cylinder strength, and (iii) the third to develop a database of images. For the image acquisition, each cylinder of the third set is cut into three slices using a stone cutting saw, resulting in six faces and a total of 324 images (6 × 54). Photos (digital images) are then taken in fully controlled lighting conditions from a height of 600 mm between the concrete surface and the camera lens. The acquired images are pre-processed (converted to grayscale, cropped, and resized to 256 × 256 pixels), and the statistical features are extracted to predict the fc and ft by using ANN and ANFIS techniques. Finally, the predicted values are tested and validated through nondestructive testing methods. The actual values of the compressive and the tensile strength of concrete were compared to the corresponding values estimated by the proposed methods, i.e. (IP, ANN) and (IP, ANFIS). The accuracy of the results largely depends on the data set. The accuracy obtained by IP/ANN is 99.7% while the one obtained with IP/ANFIS is 97.8%.
Research Article
J33. 3D simulation models for developing digital twins of heritage structures: challenges and strategies
Authors:
A. Shabani*, M. Skamantzari, S. Tapinaki, A. Georgopoulos, V. Plevris and M. Kioumarsi
Journal:
Procedia Structural Integrity
Publisher:
Elsevier
Volume: 37
Pages:
314–320
Year: 2022
(First online: 22 February 2022)
Structural vulnerability assessment of heritage structures is a pivotal part of a risk mitigation strategy for preserving these valuable assets for the nations. For this purpose, developing digital twins has gained much attention lately to provide an accurate digital model for performing finite element (FE) analyses. Three-dimensional (3D) geometric documentation is the first step in developing the digital twin, and various equipment and methodologies have been developed to facilitate the procedure. Both aerial and terrestrial close-range photogrammetry can be combined with 3D laser scanning and geodetic methods for the accurate 3D geometric documentation. The data processing procedure in these cases mostly focuses on developing detailed, accurate 3D models that can be used for the FE modeling. The final 3D surface or volumes are produced mainly by combining the 3D point clouds obtained from the laser scanner and the photogrammetric methods. 3D FE models can be developed based on the geometries derived from the 3D models using FE software packages. As an alternative, developed 3D volumes provided in the previous step can be directly imported to some FE software packages. In this study, the challenges and strategies of each step are investigated by providing examples of surveyed heritage structures.
Research Article
J32. On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm
Authors:
M.E.A. Ben Seghier*, J.A.F.O. Correia, J. Jafari-Asl, A. Malekjafarian, V. Plevris and N-T. Trung
Journal:
Neural Computing and Applications
Publisher:
Springer Nature
Volume: 33, Issue: 23
Pages:
15969–15985
Year: 2021
(First online: 16 June 2021)
Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.
Research Article
J31. Prediction of Seismic Damage Spectra using Computational Intelligence Methods
Authors:
S. Gharehbaghi, M. Gandomi, V. Plevris and A.H. Gandomi*
Journal:
Computers & Structures
Publisher:
Elsevier
Volume: 253
Article ID:
106584
Year: 2021
(First online: 01 June 2021)
Predicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures.
Research Article
J30. Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization
Authors:
V. Plevris*, N.P. Bakas and G. Solorzano
Journal:
Applied Sciences
Publisher:
MDPI
Volume: 11, Issue: 11
Article ID:
5053
Page count:
28
Year: 2021
(First online: 29 May 2021)
A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.
Research Article
J29. ITSO: A novel Inverse Transform Sampling-based Optimization algorithm for stochastic search
Authors:
N.P. Bakas*, V. Plevris, A. Langousis and S.A. Chatzichristofis
Journal:
Stochastic Environmental Research and Risk Assessment
Publisher:
Springer Nature
Volume: 36, Issue: 1
Pages:
67–76
Year: 2022
(First online: 26 April 2021)
Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI’s theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum.
Research Article
J28. A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments
Authors:
J. Jafari-Asl, M.E.A. Ben Seghier*, S. Ohadi, Y. Dong and V. Plevris
Journal:
Modelling
Publisher:
MDPI
Volume: 2, Issue: 1
Pages:
63–77
Year: 2021
(First online: 07 February 2021)
In this work, the performance of reliability methods for the probabilistic analysis of local scour at a bridge pier is investigated. The reliability of bridge pier scour is one of the important issues for the risk assessment and safety evaluation of bridges. Typically, the depth prediction of bridge pier scour is estimated using deterministic equations, which do not consider the uncertainties related to scour parameters. To consider these uncertainties, a reliability analysis of bridge pier scour is required. In the recent years, a number of efficient reliability methods have been proposed for the reliability-based assessment of engineering problems based on simulation, such as Monte Carlo simulation (MCS), subset simulation (SS), importance sampling (IS), directional simulation (DS), and line sampling (LS). However, no general guideline recommending the most appropriate reliability method for the safety assessment of bridge pier scour has yet been proposed. For this purpose, we carried out a comparative study of the five efficient reliability methods so as to originate general guidelines for the probabilistic assessment of bridge pier scour. In addition, a sensitivity analysis was also carried out to find the effect of individual random variables on the reliability of bridge pier scour.
Research Article
J27. Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks
Authors:
A. Ahmad, V. Plevris* and Q.Z. Khan
Journal:
Crystals
Publisher:
MDPI
Volume: 10, Issue: 9
Article ID:
811
Page count:
22
Year: 2020
(First online: 14 September 2020)
Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.
Research Article
J26. Structural Vulnerability Assessment of Heritage Timber Buildings: A Methodological Proposal
Authors:
A. Shabani*, M. Kioumarsi*, V. Plevris and H. Stamatopoulos
Journal:
Forests
Publisher:
MDPI
Volume: 11, Issue: 8
Article ID:
881
Page count:
20
Year: 2020
(First online: 13 August 2020)
The conservation of heritage structures is pivotal not only due to their cultural or historical importance for nations, but also for understanding their construction techniques as a lesson that can be applied to contemporary structures. Timber is considered to be the oldest organic construction material and is more vulnerable to environmental threats than nonorganic materials such as masonry bricks. In order to assess the structural vulnerability of heritage timber structures subjected to different types of risk, knowledge about their structural systems and configurations, the nature and properties of the materials, and the behavior of the structure when subjected to different risks, is essential for analysts. In order to facilitate the procedure, different assessment methods have been divided into the categories in situ and ex situ, which are applicable for vulnerability assessments at the element and full-scale level of a case study. An existing methodology for structural vulnerability assessments and conservation of heritage timber buildings is reviewed and a new methodology is proposed.
Research Article
J25. A comparative study of differential evolution variants in constrained structural optimization
Authors:
M. Georgioudakis and V. Plevris*
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 6
Article ID:
102
Year: 2020
(First online: 09 July 2020)
Differential evolution (DE) is a population-based metaheuristic algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such algorithms make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. DE is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems. In the field of structural engineering, most real-world optimization problems are associated with one or several constraints. Constrained optimization problems are often challenging to solve due to their complexity and high nonlinearity. In this work we examine the performance of several DE variants, namely the traditional DE, the composite DE (CODE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (JDE and SADE), for handling constrained structural optimization problems associated with truss structures. The performance of each DE variant is evaluated by using five well-known benchmark structures in 2D and 3D. The evaluation is done on the basis of final optimum result and the rate of convergence. Valuable conclusions are obtained from the statistical analysis which can help a structural engineer in practice to choose the suitable algorithm for these kind of problems.