Article
Multidisciplinary Sciences
Melda Yucel, Sinan Melih Nigdeli, Gebrail Bekdas
Summary: This study presents optimization approaches for structural engineering, focusing on minimizing weight. Different metaheuristic algorithms were developed and compared, and the results showed that all versions of the proposed algorithms were successful in minimizing structural weight, outperforming previous studies and algorithms.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Mahdi Azizi, Uwe Aickelin, Hadi A. Khorshidi, Milad Baghalzadeh Shishehgarkhaneh
Summary: This paper considers the shape and size optimization of truss structures using Chaos Game Optimization (CGO), which is a recently developed metaheuristic algorithm. The principles of chaos theory and fractal configuration are considered as inspirational concepts.
JOURNAL OF ADVANCED RESEARCH
(2022)
Article
Engineering, Civil
Rafiq Awad
Summary: The political optimizer algorithm has been successfully implemented in structural optimization, showing promising performance in balancing exploration and exploitation through competitive population partitioning and recent past-based position updating strategy. It outperforms previous state-of-the-art methodologies in small/medium-scale structural systems, demonstrating improved optimized weight, algorithmic stability, and convergence speeds.
Article
Computer Science, Interdisciplinary Applications
Harun Gezici, Haydar Livatyali
Summary: In this study, Harris hawks optimization (HHO) is hybridized with 10 different chaotic maps to improve its performance. The results show that chaotic maps enhance the efficiency of HHO, with the piecewise map method being the most effective one. Comparisons with other metaheuristic algorithms demonstrate that the proposed chaotic HHO algorithm successfully solves engineering problems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Engineering, Civil
Damir Sedlar, Zeljan Lozina, Ivan Tomac
Summary: This paper explores the application of VNS and its extensions to the optimization of truss structures with discrete cross sections, showcasing their effectiveness through various fixed geometry truss examples.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
A. Kaveh, F. Rajabi
Summary: This study presents a new hybrid algorithm, Migration-Based Imperialist Competitive Algorithm (MBICA), which combines the advantageous features of Imperialist Competitive Algorithm (ICA) and Biogeography-Based Optimization (BBO) to establish an effective search technique. Compared to other methods, MBICA converges faster and achieves better solutions in structural optimization.
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Betul Sultan Yildiz, Pranav Mehta, Natee Panagant, Sadiq M. Sait, Seyedali Mirjalili, Ali Riza Yildiz
Summary: A novel metaheuristic algorithm called Chaotic Marine Predators Algorithm (CMPA) is proposed for engineering problem optimization, which integrates the exploration merits of MPA and the exploitation capabilities of chaotic maps. The proposed algorithm is applied to decode complex design and manufacturing problems and its performance is evaluated on CEC 2020 numerical problems and constrained design problems. Additionally, case studies and statistical analysis are conducted to compare CMPA with other algorithms, showing its significantly improved performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Civil
Farqad K. J. Jawad, Mohammed Mahmood, Dansheng Wang, Osama AL-Azzawi, Anas Al-Jamely
Summary: The paper aims to design truss structures using the Dragonfly Algorithm, a nature-inspired optimization algorithm. Modifications to the algorithm were proposed to solve discrete optimization problems for steel trusses, demonstrating the algorithm's effectiveness and robustness in achieving the lightest weight with minimal structural analysis.
Article
Mathematics
Khalid Abdulaziz Alnowibet, Shalini Shekhawat, Akash Saxena, Karam M. Sallam, Ali Wagdy Mohamed
Summary: Metaheuristics, including bio-inspired ones, have been widely used to solve complex optimization problems. In this paper, a variant of the popular Whale Optimization Algorithm (WOA) called the Augmented Whale Optimization Algorithm (AWOA) is proposed. The AWOA incorporates opposition-based learning and Cauchy mutation operator to improve the exploration and exploitation capabilities of WOA. Experimental results and analyses demonstrate that the proposed AWOA outperforms the original WOA and achieves better optimization performance for both benchmark functions and real-world problems.
Article
Computer Science, Artificial Intelligence
Huawei Tong, Yun Zhu, Juliano Pierezan, Youyun Xu, Leandro dos Santos Coelho
Summary: The study proposes a new algorithm called Chaotic Coyote Optimization Algorithm (CCOA), which outperforms the original Coyote Optimization Algorithm (COA) in terms of global convergence speed.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Engineering, Civil
Ali Kaveh, Seyed Milad Hosseini, Ataollah Zaerreza
Summary: The Jaya algorithm is a simple and efficient population-based metaheuristic algorithm, but has shortcomings such as premature convergence and inadequate population diversity. The proposed Improved Shuffled based Jaya (IS-Jaya) algorithm enhances exploration capability through shuffling process, making it an effective tool for solving discrete size optimization problems.
Article
Mathematics
Tsu-Yang Wu, Ankang Shao, Jeng-Shyang Pan
Summary: Metaheuristic algorithms are important in the field of artificial intelligence, and the tumbleweed optimization algorithm (TOA) is a new algorithm that mimics the growth and reproduction of tumbleweeds. Chaotic maps have been proven to be an improved method for optimization algorithms, and this paper presents a chaotic-based TOA (CTOA) that incorporates chaotic maps into the optimization process. The CTOA aims to improve population diversity, global exploration, and prevent falling into local optima. The performance of CTOA is tested using 28 benchmark functions, and the circle map is found to be the most effective in improving accuracy and convergence speed, especially in 50D.
Article
Engineering, Civil
Ali Kaveh, Ali Shabani Rad
Summary: In this study, the algebraic force method (AFM) is used for optimal design of truss structures using meta-heuristics algorithms. The AFM reduces necessary CPU time compared to the displacement approach. The results show significant time reduction when using AFM for structural weight optimization in cases where DSI is smaller than DKI. Additionally, AFM is more easily applicable for discrete structural analysis due to its amenability, simplicity, and generality.
Article
Engineering, Civil
H. Dadashi, M. Mohammadi
Summary: A novel metaheuristic optimization algorithm called RUPSO is proposed in this research, based on the well-known Particle Swarm Optimization algorithm. The RUPSO algorithm has been shown to converge to better optimum solutions compared to PSO and other existing metaheuristic algorithms. The random updating process used in RUPSO decreases the sensitivity of the algorithm and increases its stability, allowing it to find optimized answers with high convergence rates for problems with discrete or continuous variables.
Article
Computer Science, Interdisciplinary Applications
Mahmoud Alimoradi, Hossein Azgomi, Ali Asghari
Summary: This paper presents a new metaheuristic algorithm called Trees Social Relations Optimization Algorithm (TSR) inspired by the hierarchical and collective life of trees in the jungle. TSR uses trees and sub-jungles to represent solutions and employs parallel and synchronized sub-jungles with dedicated operators to increase accuracy and reduce response time. Experimental results demonstrate that TSR algorithm provides appropriate and acceptable answers in both time and accuracy.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Mathematics, Interdisciplinary Applications
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Gabriel Trierweiler Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Efficient models for short-term load forecasting in electricity distribution and generation systems are crucial for companies' energetic planning. In this study, an ensemble learning model based on dual decomposition approach, machine learning models and hyperparameters optimization is proposed. The model successfully decomposes the time series and handles the non-linearities, and achieves accurate load forecasting results with reduced errors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Energy & Fuels
Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The cost of electricity and gas has a direct impact on people's everyday routines, but the value of electricity is closely related to spot market prices, which can increase in winter due to higher energy demand. Existing models for forecasting energy costs are not robust enough due to competition, seasonal changes, and other variables. This study proposes combining seasonal and trend decomposition using LOESS and Facebook Prophet methodologies to improve the accuracy of analyzing time series data on Italian electricity spot prices.
Article
Energy & Fuels
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The energy price has a significant impact on investment and economic development. Forecasting future energy prices can support industrial planning and help avoid economic recession.
Article
Thermodynamics
Silvio Cesar de Lima Nogueira, Stephan Hennings Och, Luis Mauro Moura, Eric Domingues, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study aimed to develop a reliable framework for predicting diesel engine emissions by using a novel Random Forest (RF) model. The RF model accurately predicted the output signals of the diesel engine, demonstrating its viability, effectiveness, and competitive performance.
Article
Computer Science, Information Systems
Andre Armstrong Janino Cizotto, Rodrigo Clemente Thom de Souza, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The objective of this study is to validate the use of deep neural networks (DNNs) for segmenting and classifying web elements. A dataset of 2200 images representing 10 distinct classes was created using screenshots of real web pages. The study contributes by validating classification-only convolutional neural networks (CNNs) with the support of Class Activation Mapping (CAM), a weakly-supervised semantic segmentation technique. The best-performing model achieved a final accuracy rating of 95.71%, but improvements are still needed on the dataset and architecture for real-time dynamic web page building.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Insulators installed outdoors are prone to accumulation of contaminants, causing increased conductivity and leakage current, eventually leading to flashover. To enhance power system reliability, it is possible to predict fault development and potential shutdown by evaluating the increase in leakage current. This paper proposes a method, optimized EWT-Seq2Seq-LSTM with attention, which combines empirical wavelet transform (EWT) to reduce non-representative variations and the attention mechanism with LSTM recurrent network for prediction. The model achieved a 10.17% lower mean square error (MSE) compared to standard LSTM and a 5.36% lower MSE compared to the model without optimization, demonstrating the effectiveness of the attention mechanism and hyperparameter optimization.
Article
Chemistry, Analytical
Andressa Borre, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The dataset is used to train a hybrid CNN-LSTM architecture, which employs quantile regression to manage uncertainties in the data. The results show that this approach outperforms traditional reference models, making it beneficial for companies to optimize maintenance schedules and improve the performance of their electric machines.
Article
Computer Science, Artificial Intelligence
Alan Naoto Tabata, Alessandro Zimmer, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study used synthetic datasets from the CARLA simulator and real-world dataset from WAYMO Open to train and evaluate computer vision algorithms. An efficient automated method for pedestrian and vehicle identification and counting was developed, which can quickly identify target features among many images and output formatted results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper evaluates a time series of leakage current from a high-voltage laboratory experiment using porcelain pin-type insulators. Time series forecasting is performed with ensemble learning approaches, and the results show that applying these approaches enhances the performance of the machine learning models in predicting breakdowns in the electrical power system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Computer Science, Interdisciplinary Applications
Jose Pedro G. Carvalho, Denis E. C. Vargas, Breno P. Jacob, Beatriz S. L. P. Lima, Patricia H. Hallak, Afonso C. C. Lemonge
Summary: This paper formulates a multi-objective structural optimization problem and utilizes multiple evolutionary algorithms to solve it. By optimizing the grouping of structural members, the best truss structure can be found. After analyzing various benchmark problems, the study reveals the existence of competitive structural member configurations beyond symmetry-based groupings.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Se-Hyeon Kang, Hyun-Seok Kim, Seonho Cho
Summary: This paper investigates shape identification using peridynamic theory and gradient-based optimization. The particle-based and non-local characteristics of peridynamics allow for direct interface modeling, avoiding remeshing difficulties. The boundary of scatterers is parameterized using B-spline surfaces, and design sensitivity is obtained using an efficient adjoint variable method. The accuracy and efficiency of the proposed method are verified through numerical examples.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Laura Rio-Martin, A. Prieto
Summary: Any numerical procedure in mechanics requires selecting an appropriate constitutive model for the material. The common assumptions for linear wave propagation in viscoelastic materials include the standard linear solid, Maxwell, Kelvin-Voigt, and fractional derivative models. Typically, the intrinsic parameters of the mathematical model are estimated based on available experimental data to fit the mechanical response of the chosen constitutive law. However, this approach may suffer from the uncertainty of inadequate model selection. In this work, the mathematical modeling and selection of frequency-dependent constitutive laws for linear viscoelastic materials are solely performed based on experimental measurements without imposing any functional frequency dependence. This data-driven methodology involves solving an inverse problem for each frequency.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Pramod Kumar Gupta, Chandrabhan Singh
Summary: In this paper, a novel algorithm is developed to generate the geometrical model of coarse aggregate, and it is further applied in the generation of a finite element model for concrete. Through numerical simulation and comparison with existing literature, the effectiveness of the meso-model is verified.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiao Wang, Qingrui Yue, Xiaogang Liu
Summary: This study proposes a graph neural networks-based method to recover the missing connection information in crack meshes, and comparative analysis shows that the trained GraphSAGE outperforms other GNNs on triangular meshing task, revealing the potential of GNNs in restoring missing information.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Dhiraj S. Bombarde, Manish Agrawal, Sachin S. Gautam, Arup Nandy
Summary: The study introduces a novel twenty-seven node quadratic EAS element, addressing the underutilization of quadratic elements in existing 3D EAS elements. Additionally, a six-node wedge and an eighteen-node wedge EAS element are presented in the manuscript.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Hau T. Mai, Seunghye Lee, Joowon Kang, Jaehong Lee
Summary: In this work, an effective Damage-Informed Neural Network (DINN) is developed for pinpointing the position and extent of structural damage. By using a deep neural network and Bayesian optimization algorithm, the proposed method outperforms other algorithms in terms of accuracy and efficiency.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Qingsong Xiong, Qingzhao Kong, Haibei Xiong, Lijia Liao, Cheng Yuan
Summary: This study proposes a novel physics-informed deep 1D convolutional neural network (SSM-CNN) for enhanced seismic response modeling. By construing the differential nexus of state variables derived from the state-space representation of initial structural response, an innovative parameter-free physics-constrained mechanism is designed and embedded for performance enhancement. Experimental validations confirmed the effectiveness and superiority of physics-informed SSM-CNN in seismic response prediction.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
D. Herrero-Perez, S. G. Pico-Vicente
Summary: This work presents an efficient, flexible, and scalable strategy for implementing density-based topology optimization formulation in fail-safe structural design. The use of non-overlapping domain decomposition, adaptive mesh refinement, and computing buffers allows for successful evaluation of fault cases.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiangyang Cui, Gongcheng Peng, Qi Ran, Huan Zhang, She Li
Summary: A novel degenerated shell element called MITC4+R is developed, which eliminates various locking problems common to shell elements and significantly improves the computational efficiency. It is based on assumed natural strain method and introduces a physical stabilization term.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Shouyan Jiang, Wangtao Deng, Ean Tat Ooi, Liguo Sun, Chengbin Du
Summary: This study presents an innovative data-driven algorithm that combines the scaled boundary finite element method and a deep learning framework for identifying crack-like defects in large-scale structures. The proposed algorithm accurately determines the number, location, and depth of cracks and is robust to noise. It provides valuable insight into the detection and diagnosis of structural defects.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Shiqiang Qin, Jiacheng Feng, Jian Tang, Xuejin Huo, Yunlai Zhou, Fei Yang, Magd Abdel Wahab
Summary: This study assesses the condition of a CFST arch bridge using in-situ vibration measurements, finite element model updating, and an improved artificial fish swarm algorithm. The results indicate that the bridge has good dynamic performance, but track conditions need improvement before operation.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Md. Imrul Reza Shishir, Alireza Tabarraei
Summary: In this paper, a density-based topology optimization method using neural networks is proposed for designing multi-material domains under combined thermo-mechanical loading. The method achieves automatic sensitivity analysis and removes the need for other optimization algorithms. Experimental results show that the method can handle high-resolution re-sampling, resulting in more refined and smooth optimal topologies.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bartosz Sobczyk, Lukasz Pyrzowski, Mikolaj Miskiewicz
Summary: This paper describes the problems encountered during the analysis of the structural response of historic masonry railroad arch bridges. It focuses on the stiffness of the masonry arches, their strengths, and the estimation of railroad load intensity. The paper presents computational models created to efficiently describe the responses of the bridges under typical loading conditions and discusses the outcomes of nonlinear static analyses. The possible causes of the deterioration of the bridges' condition were identified through these analyses.
COMPUTERS & STRUCTURES
(2024)
Article
Computer Science, Interdisciplinary Applications
T. Koudelka, T. Krejci, J. Kruis
Summary: This paper presents a numerical model for the coupled hydro-mechanical behaviour of partially saturated soils, and demonstrates its effective application through a numerical example.
COMPUTERS & STRUCTURES
(2024)
