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A. Shabani*, M. Skamantzari, S. Tapinaki, A. Georgopoulos, V. Plevris and M. Kioumarsi, “3D simulation models for developing digital twins of heritage structures: challenges and strategies”, Procedia Structural Integrity, 37(2022), pp. 314-320, 2022. DOI: 10.1016/j.prostr.2022.01.090

Abstract:
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.



Keywords:
3D geometric documentation; cultural heritage; digital twins; 3D laser scanner; photogrammetry; finite element model

I. Faridmehr, M. Shariq, V. Plevris*, and N. Aalimahmoody, “Novel Hybrid Informational Model for Predicting the Creep and Shrinkage Deflection of Reinforced Concrete Beams Containing GGBFS”, Neural Computing and Applications, 2022. DOI: 10.1007/s00521-022-07150-3

Abstract:
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.



Keywords:
GGBFS; Creep and shrinkage deflection; Neural networks; Whale optimization algorithm.

V. Plevris*, N. D. Lagaros and A. Zeytinci, “Blockchain in Civil Engineering, Architecture and Construction Industry: State of the Art, Evolution, Challenges and Opportunities”, Frontiers in Built Environment, 8:840303, 2022. DOI: 10.3389/fbuil.2022.840303

Abstract:
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.



Keywords:
blockchain, general purpose technology (GPT), distributed ledger, civil engineering, architecture, construction, engineering.

V. Plevris and G. Solorzano*, “A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking”, Data, 7(4), Article ID 46, 52 pages, 2022. DOI: 10.3390/data7040046

Abstract:
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.



Keywords:
optimization; unconstrained; benchmark functions; objective function; GA; PSO; SQP.

 

N.D. Lagaros, V. Plevris and N.A. Kallioras, "The mosaic of metaheuristic algorithms in structural optimization", Archives of Computational Methods in Engineering (State of the art reviews), 2022.

Abstract:
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.

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