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optimization of prestressed concrete bridge girder section using a modified harmony search algorithm

STRUCTURES 2022年 46卷 625-636页

作者： Jahjouh, Mahmoud Erhan, Semih Univ Bahrain Civil Engn Dept Sakhir Bahrain

Precast I girders provide rapid construction and structural simplicity, making them an increasingly popular choice for bridge construction. The current standard sections adopted by the American Association of State Highway and Transportation Officials (AASHTO) are not SI friendly and may be improved utilizing increasingly efficient metaheuristic optimization algorithms. Thus, this research study aims to obtain the optimum shape of Precast I (PCI) bridge girders commonly used in bridge construction due to various advantages. The optimization problem is solved using a modified metaheuristic optimization algorithm considering a number of constraints that are stipulated by the AASHTO LRFD code requirements. To show the capabilities of the utilized modified optimization algorithm, a comparison is performed with its baseline counterpart. The structural design optimization of bridge superstructures is performed with the objective of minimizing the total cost of the resulting superstructure. Consequently, the most economical shape of the PCI girders is obtained. The results of this analysis are then compared AASHTO and the California Department of Transportation (Caltrans) PCI girders. The new I girder sections exhibits 13-17% and 5-16% better structural efficiency factor (rho) as well as 14-19% and 4-21% structural efficiency ratio (alpha) than Caltrans and AASHTO girders, respectively. Thus, new sections for PCI girders are developed having various heights to facilitate more economical design of bridges having various geometric properties such as span length, girder spacing and slab thickness.

关键词： Bridge engineering Bridge Girder optimization Harmony search optimization algorithms

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Global Convergence and Asymptotic Optimality of the Heavy Bell Method for a Class of Nonconvex optimization Problems

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 2449-2454页

作者： Ugrinovskii, V Petersen, I. R. Shames, I Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia Australian Natl Univ Sch Engn CIICADA Lab Canberra ACT 2601 Australia

In this letter we revisit the famous heavy ball method and study its global convergence for a class of non-convex problems with sector-bounded gradient. We characterize the parameters that render the method globally convergent and yield the best R-convergence factor. We show that for this family of functions, this convergence factor is superior to the factor obtained from the triple momentum method.

关键词： optimization algorithms robust control

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Enhanced efficiency and quality in wind turbine gearbox assembly: a new parallel assembly sequence planning (PASP) model

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 2024年第1期17卷 1048-1065页

作者： Mutale, Sydney Wang, Yong Yasir, Jan North China Elect Power Univ Sch New Energy Changping Beijing Peoples R China Univ Zambia Sch Engn Lusaka Zambia

This paper presents two major advances in parallel assembly sequence planning (PASP) for complex systems, specifically wind turbine gearboxes. The proposed time-cost-quality PASP hybrid model (PASP-TCQ) aims to enhance efficiency, reduce costs, and improve quality by aligning optimisation parameters with real-world demands. This model is designed to optimise complex assembly processes by addressing constraints on time, cost, and quality effectively. Additionally, we introduce a particle swarm-bacteria foraging optimisation (PSBFO) algorithm that integrates the global search capability of particle swarm optimisation (PSO) with the local optimisation strengths of bacteria foraging optimisation (BFO). Integrating PSBFO into PASP-TCQ achieves significant improvements: a 17% reduction in assembly time to 100 hours, a 10% cost reduction to $94,500, and a quality index improvement to 0.93. Statistical tests, including analysis of variance (ANOVA) and tukey's honest significant difference (HSD), confirmed the PSBFO's superiority, with significant gains of 8.44 units over BFO and 13.02 units over PSO in objective function values (p < 0.05). Extensive simulations on a 10 MW wind turbine gearbox validate the effectiveness of the PASP-TCQ and PSBFO, demonstrating their potential to enhance efficiency and productivity in industrial assembly operations.

关键词： Parallel assembly sequence planning time-cost-quality hybrid model particle swarm-bacteria foraging optimisation optimization algorithms industrial assembly efficiency wind turbine gearboxes

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Optimal allocation of electric vehicle charging stations and distributed generation in radial distribution networks

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 60卷

作者： Soliman, Ismail A. Tulsky, Vladimir Abd el-Ghany, Hossam A. Elgebaly, Ahmed E. Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta Egypt NRU Moscow Power Engn Inst Elect Power Syst Dept Moscow 111250 Russia

The widespread spread of electric vehicles requires the establishment of charging stations (EVCSs), and this is considered a large load on the network. This gives priority to distributing the stations in a way that reduces the load on the network, and in parallel, re-planning the network and supplying it with the necessary energy to maintain energy efficiency and power quality. Total energy loss minimization, voltage deviation minimization, and voltage stability index improvement are the considered power quality indices in the multi-objective function. Vehicle to grid (V2G) feature, distributed generation units (DG), and capacitor banks are used for improving system performance, by injecting the required active and reactive power. In addition, the initial and running costs of V2G, DG units, and capacitor banks are considered in the multi-objective function. The optimal sizing and allocation of charging stations, V2G, DG units, and capacitor banks are performed using a proposed SelfAdaptive Multi-Population Elitist JAYA (SAMPE-JAYA) algorithm and checked using the genetic algorithm (GA). The proposed algorithm is tested using various scenarios, two standard IEEE test systems. To emphasize the effectiveness and applicability of the proposed algorithm, it is applied on a real-world distribution system. To accommodate the optimal allocation of EVCS, which constitute 80.7% and 78.9% of the base active load for the IEEE 33 and 69 bus systems, respectively. Integrating DG amounting to 17.35 % and 18.25 % of the base load is necessary. Additionally, capacitor banks, contributing 36.84 % and 31.166% of the reactive power load, are also required for effective voltage support and system reliability in each respective case.

关键词： Electric vehicle charging stations optimization algorithms Vehicle to grid Power quality Capacitors allocation Distributed generation

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A variable sampling interval EWMA control chart for urinary tract infections data

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2024年第12期94卷 2727-2753页

作者： Bai, Yajie Chiang, Jyun-You Wang, Shuai Mou, Zhengcheng Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China Hainan Univ Sch Econ 58 Renmin Ave Haikou 570228 Hainan Peoples R China

Control chart is an important method in Statistical Process Control, which is used to study how a process changes over time. The exponential distribution is one of the commonly used models for fitting data, especially in areas such as component life, aviation accidents, or health-care processes. In order to further improve the sensitivity of the control chart, we propose an exponentially weighted moving average control chart with a variable sampling interval scheme, denoted as VSIEWMA-T control chart, for monitoring exponentially distributed quality characteristics (e.g. Urinary Tract Infections data). In this paper, the average time to signal property of the proposed control chart is investigated using the Markov chain method. In addition, parameter optimization algorithms for known or unknown shift levels are provided. Subsequently, numerical analysis shows that the proposed control chart outperforms other competitive control charts. Finally, an example of urinary tract infections data is provided for illustration.

关键词： Exponentially weighted moving average chart variable sampling interval exponential distribution Markov chain method optimization algorithms

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On Robustness in optimization-Based Constrained Iterative Learning Control

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 2846-2851页

作者： Liao-McPherson, Dominic Balta, Efe C. Rupenyan, Alisa Lygeros, John Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland Inspire AG Automat Optimierung & Mechatron English Automat O CH-8005 Zurich Switzerland

Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. optimization-based ILC (OB-ILC) is a powerful design framework for constrained ILC where measurements from the process are integrated into an optimization algorithm to provide robustness against noise and modelling error. This paper proposes a robust ILC controller for constrained linear processes based on the forward-backward splitting algorithm. It demonstrates how structured uncertainty information can be leveraged to ensure constraint satisfaction and provides a rigorous stability analysis in the iteration domain by combining concepts from monotone operator theory and robust control. Numerical simulations of a precision motion stage support the theoretical results.

关键词： optimization Convergence Uncertainty Robustness Task analysis Robust control Noise measurement optimization algorithms robust control iterative learning control manufacturing systems and automation

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QPALM-OCP: A Newton-Type Proximal Augmented Lagrangian Solver Tailored for Quadratic Programs Arising in Model Predictive Control

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1349-1354页

作者： Lowenstein, Kristoffe Fink Bernardini, Daniele Patrinos, Panagiotis Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy ODYS Srl I-20159 Milan Italy Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

In model predictive control fast and reliable quadratic programming solvers are of fundamental importance. The inherent structure of the subsequent optimal control problems can lead to substantial performance improvements if exploited. Therefore, we present a structure-exploiting solver based on proximal augmented Lagrangian, extending the general-purpose quadratic programming solver QPALM. Our solver relies on semismooth Newton iterations to solve the inner sub-problem while directly accounting for the optimal control problem structure via efficient and sparse matrix factorizations. The matrices to be factorized depend on the active-set and therefore low-rank factorization updates can be employed like in active-set methods resulting in cheap iterates. We compare our solver with QPALM and other well-known solvers and show its benefits in a numerical example.

关键词： optimization algorithms optimal control predictive control for nonlinear systems

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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Tao, Hai Abba, Sani I. Al-Areeq, Ahmed M. Tangang, Fredolin Samantaray, Sandeep Sahoo, Abinash Siqueira, Hugo Valadares Maroufpoor, Saman Demir, Vahdettin Bokde, Neeraj Dhanraj Goliatt, Leonardo Jamei, Mehdi Ahmadianfar, Iman Bhagat, Suraj Kumar Halder, Bijay Guo, Tianli Helman, Daniel S. Ali, Mumtaz Sattar, Sabaa Al-Khafaji, Zainab Shahid, Shamsuddin Yaseen, Zaher Mundher Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Qiannan Normal Univ Nat Inst Big Data Applicat & Artificial Intelligence Duyun 558000 Guizhou Peoples R China INTI Int Univ Fac Data Sci & Informat Technol Nilai Malaysia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Membrane & Water Secur Dhahran 31261 Saudi Arabia Univ Kebangsaan Malaysia Fac Sci & Technol Dept Earth Sci & Environm Bangi 43600 Selangor Malaysia Natl Inst Technol Dept Civil Engn Srinagar 190006 J&k India Odisha Univ Technol & Res Dept Civil Engn Bhubaneswar Orissa India Univ Tecnol Fed Parana Grad Program Comp Sci Grad Program Ind Engn Campus Ponta Grossa Curitiba PR Brazil Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran KTO Karatay Univ Dept Civil Engn TR-42020 Konya Turkiye Aarhus Univ Ctr Quantitat Genet & Genom DK-8000 Aarhus Denmark Univ Fed Juiz de Fora Dept Computat & Appl Mech BR-36036900 Juiz De Fora MG Brazil Shahid Chamran Univ Ahvaz Fac Civil Engn & Architecture Dept Civil Engn Ahvaz Iran Univ Prince Edward Isl Canadian Ctr Climate Change & Adaptat St Peters PE Canada Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Nasiriyah 64001 Thi Qar Iraq Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Coll Micronesia FSM Educ Div Yap Campus Ponape Micronesia Univ Southern Queensland UniSQ Coll Toowoomba Qld 4350 Australia Al Mustaqbal Univ Bldg & Construct Tech Engn Dept Hillah 51001 Babil Iraq Al Turath Univ Coll Baghdad Iraq Univ Teknol Malaysia Dept Water & Environm Engn Johor Baharu 81310 Malaysia King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia

River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000-2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input pa-rameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication.

关键词： Data availability Machine learning Nature-inspired algorithms River flow modeling optimization algorithms

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Evaluation Of Fracture Energy Of Concrete By Hybrid SVR Analysis

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第12期27卷 3645-3661页

作者： Jiang, Pan Ge, Nan Hebei Univ Water Resources & Elect Engn Dept Civil Engn Cangzhou 061001 Peoples R China North China Univ Sci & Technol Coll Civil & Architectural Engn Tangshan 063210 Peoples R China

Regression techniques were developed to determine the concrete initial (Gf) and total (GF) fracture energy based on prior data using mechanical features and mixed design elements. There were 264 samples retrieved from prior investigations in all. Research contributes to the field by improving the accuracy of predicting concrete fracture energy, offering a methodology for optimizing hyperparameters, and providing a model comparison that demonstrates the practical value of the new approach. These findings can benefit the construction and engineering industries by enhancing the accuracy of material property predictions and improving the quality and safety of constructed structures. This study merged support vector regression (SVR) assessment with arithmetic optimization algorithm (AOA) and whale optimization algorithm (WOA) to predict the Gf and GFF of concrete. The aim of combining the optimization algorithms with SVR analysis was to determine the optimal values of hyperparameters that play pivotal role in developed models' accuracy. The computation and analysis for Gf and GF using five criteria shows that optimized SVR-AOA and SVR-WOA analyses can do admirably well throughout the forecasting model. When the outperforming SVR analysis was compared to the library, it was discovered that the newly constructed SVR-AOA also present a small raise in accuracy, with modification in all metrics. In conclusion, while the SVR-WOA demonstrates its effectiveness in the forecasting outline, the SVR-AOA analysis appears to be a reliable approach for determining accurate Gf values (R2train = 0.921, and R2test = 0.9853) and GF values (R2train = 0.9281, and R2test = 0.9236, as supported by the arguments and feasibility of the models.

关键词： Concrete Fracture energy Support vector regression optimization algorithms

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Prediction of shallow foundation settlement: usage of hybridized support vector regression model

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 281-290页

作者： Yang, Xiaochuan Chongqing Coll Finance & Econ Coll Appl Technol Chongqing 402160 Peoples R China

Because of the intricacy of cohesion soil texture, settlement simulation in cohesion materials is a critical subject to address. According to the literature study, shallow foundation prediction did not much considered, so developing optimized models can enhance the forecast accuracy in this field. It is the focus of this research to put newly developed machine learning models, such as hybridized support vector regression (SVR) with firefly algorithm (FFA) and cuckoo optimization algorithm (COA), into practice as efficient approaches to predict settlement (S-m) of shallow foundations over cohesion soil properties. The footing width, footing pressure, footing geometry, count of SPT blow, and footing embedment ratio are chosen as estimation parameters. The use of optimization techniques served the aim of determining the ideal value for the major variables of the investigated model. In the COA-SVR system, the values of R-2 and RMSE in the learning phase are 0.9649 and 4.7693, suitable than ANFIS-PSO by 0.9025 and 8.09, and in the examining phase are 0.9937 and 2.5485, considerably proper compared to ANFIS-PSO at 0.739 and 14.10, respectively. By considering another metric-like PI index, the COA-SVR network results more properly than the FFA-SVR model, with a decline of 0.0206 and 0.0717 in the learning and examining data sets, respectively. Furthermore, VAF index also depicts the same trend with outperforming the COA-SVR to FFA-SVR, especially in the examining stage, with a rise of about 3.2%. In conclusion, it is clear that the COA-SVR system could perform better than those integrated with FFA, as well as ANFI-PSO, where the proposed system can be known as the proposed system in the estimation procedure of shallow foundation S-m.

关键词： Shallow foundation Settlement Estimation Hybrid SVR optimization algorithms

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