Vagelis Plevris Web Site

www.vplevris.net

A. Jiménez Rios, S. Ruiz-Capel, V. Plevris and M. Nogal, “Computational Methods Applied to Earthen Historical Structures”, Frontiers in Built Environment, 9:1219108 (DOI: 10.3389/fbuil.2023.1219108), 2023.

Abstract:
Earthen structures have an important representation among the UNESCO World Heritage List sites as well as among the built environment in general. Unfortunately, earthen heritage structures are also numerous within the UNESCO List of World Heritage in Danger whereas other existing common earthen structures are extremely vulnerable to seismic and climate change events. Within the field of heritage conservation, structural analysis contributes to the safety evaluation of the structure, the diagnosis of the causes of damage and decay, and to the validation of interventions. Thus, the need to develop effective and accurate computational methods suitable for the study of both monumental and vernacular earthen structures becomes evident. This paper compiles, summarizes, and highlights the latest developments and implementations of computational methods for the study of such structure typologies. The literature has been explored following the PRISMA-S checklist methodology and a narrative synthesis was used for the presentation of results. Finally, future trends, opportunities, and challenges are discussed.



Keywords:
Adobe, rammed earth, cob, finite element method, discrete element method, limit analysis.

M. Nikoo, G. Hafeez, G. Doudak and V. Plevris, "Predicting the Fundamental Period of Light-Frame Wooden Buildings by Employing Bat Algorithm-Based Artificial Neural Network", in Artificial Intelligence and Machine Learning Techniques for Civil Engineering, V. Plevris, A. Ahmad and N.D. Lagaros (Eds.), IGI Global, pp. 139-162, 2023.

Abstract:

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.

Book: "Artificial Intelligence and Machine Learning Techniques for Civil Engineering", Eds: V. Plevris, A. Ahmad, N.D. Lagaros, IGI Global, 2023. DOI: 10.4018/978-1-6684-5643-9

Description

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering.

Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

Book: "Artificial Intelligence (AI) Applied in Civil Engineering", Eds: N.D. Lagaros and V. Plevris, MDPI, Basel, Switzerland, 698 pages, 2022.

Description

In recent years, the application of artificial intelligence (AI) in several scientific fields, varying from big data handling to medical diagnosis, has drawn significant attention. The use of AI is already present in our daily lives, as exemplified by personalized ads, virtual assistants, autonomous driving, etc. Not surprisingly, AI methodologies have demonstrated impressive results through a wide range of uses and applications in engineering fields, including civil and structural engineering. The increase in AI studies shows that the use of AI in civil engineering is gaining momentum and will keep increasing in the coming years, bringing new innovations and applications. This book collection contains applications and recent advances of AI with regard to civil engineering problems, promoting cross-fertilization between these scientific fields. In particular, the focus is on hybrid studies and applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, and construction management. The book contains 35 contributions in total from 19 different countries around the world, covering a broad range of topics related to the applications of AI in civil engineering.

M.E.A. Ben Seghier, V. Plevris* and G. Solorzano, "Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes", Structures, 44, pp. 261-273 (DOI: 10.1016/j.istruc.2022.08.007), 2022.

Abstract:
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.

Keywords:
Ultimate bending capacity, Circular steel tubes, Prediction, Random forest, Whale optimization algorithm, Performance index.

Page 3 of 21