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A framework for predicting the carbonation depth of concrete incorporating fly ash based on a least squares support vector machine and metaheuristic algorithms

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JOURNAL OF BUILDING ENGINEERING 2023年 65卷

作者： Zhang, Kai Zhang, Ke Bao, Rui Liu, Xianghua Kunming Univ Sci & Technol Fac Civil & Architectural Engn Kunming 650500 Yunnan Peoples R China China Nonferrous Met Ind Co Ltd Kunming Prospecting Design Inst Kunming 650051 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China

The carbonation of concrete structures is becoming a more serious problem as the global CO2 level increases, and poses a great challenge to concrete durability research. In this study, a hybrid prediction framework on the basis of the least squares support vector machine (LSSVM) and metaheuristic algorithms is proposed to accurately predict the carbonation depth of concrete incorporating fly ash. The fly ash replacement level, cement content, water-to-binder ratio, CO2 content, relative humidity of environmental and exposure time are treated as input variables of the framework. A database containing 500 sets of samples is created for model building and comparison. The results show that five hybrid models have superior accuracy in the prediction of concrete carbonation depth, where the R2, RMSE, MAE and VAF range from 0.9683 to 0.9819, 1.7309-2.2936 mm, 1.2255-1.5478 mm, and 96.8317 to 98.1995, respectively. In addition, the proposed prediction framework has better performance (i.e., smaller RMSE) compared to reported empirical prediction models. The Sobol' global sensitivity index analysis results indicate that the water-to-binder ratio, exposure time and cement content have higher sensitivity to predict the carbonation depth. The effect of dataset division ratio on the models (an aspect that has not been investigated in previous research) is further explored in this study, and the optimal dataset di-vision ratio for each model is determined.

关键词： Carbonation depth of concrete Least squares support vector machine (LSSVM) metaheuristic algorithms Global sensitivity analysis (GSA) Optimal division ratio

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An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models

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ENERGY CONVERSION AND MANAGEMENT 2023年 276卷

作者： Navarro, Mario A. Oliva, Diego Ramos-Michel, Alfonso Haro, Eduardo H. Univ Guadalajara Dept Innovac Basada Informac & Conocimiento CUCEI Guadalajara 44430 Mexico Univ Guadalajara Dept Innovac Basada Informac & Conocimiento DIVTIC CUCEI Guadalajara Mexico

Alternative energies will be the engine that moves the world in the future;therefore, researchers focus their attention on the problems related to designing and properly exploiting this type of energy. Artificial intelligence methods are explored as tools to improve different branches of alternative energies. In this context, metaheuristic algorithms (MA) are optimization techniques that have been explored in the field of solar cell design. This article analyses the performance of different MAs for modeling solar cells by estimating the parameters of photovoltaic (PV) diode models. The MA used are part of a recent generation of techniques that include innovative processes that permits a proper selection of the optimization operators in the iterative search;such algorithms are the Artificial Hummingbird Algorithm (AHA), Aquila Optimizer (AO), African Vulture Optimization Algorithm (AVOA), Equilibrium Optimizer (EO), Golden Eagle Optimizer (GEO), Side Blotched Lizard Algorithm (SBLA), Social Network Search (SNS), and Reptile Search Algorithm (RSA). We implemented them to optimize the parameters of different models of solar cells with arrays of one, two, and three diodes. The experimental analysis performed on an R.T.C. France solar cell demonstrates which methods are the most suitable for the identification of unknown parameters in three PV models named single diode solar cell model (SDSCM), double diode solar cell model (DDSCM), and triple diode solar cell model (TDSCM). The results were statistically analyzed with Voltage-Current and Voltage-Power tests, comparisons of the best -optimized parameters, convergence plots, four error types such as Root Mean Square Error (RMSE), Absolute Error (AE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE), a run-time analysis and non-parametric tests. The study converges in comparing and defining which of the implemented MA's is the best alternative for the design of solar cells, concluding that some test

关键词： Solar cell models Parameter estimation metaheuristic algorithms Diode models

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Optimal design of planar RC frames considering CO2 emissions using ECBO, EVPS and PSO metaheuristic algorithms

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JOURNAL OF BUILDING ENGINEERING 2020年 28卷

作者： Kaveh, A. Izadifard, R. A. Mottaghi, L. Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Imam Khomeini Int Univ Civil Engn Dept Qazvin Iran

Nowadays reduction of greenhouse gas emissions is considered in the design and construction of reinforced concrete (RC) structures. One of the techniques for minimizing CO2 emissions is the use of optimization techniques at the structural design stage. For optimal design of RC structures, in addition to considering specifications of codes and construction material costs, strategies should also be applied for minimizing CO2 emission in the construction materials production. This study investigates the relationship between optimal cost and optimal carbon dioxide emission in design for RC frames of different heights by using an automatic computational process. The main objective functions are minimizing CO2 emissions and construction cost. For this purpose, three RC frames with four, eight and twelve stories, are modeled and optimized. Results indicate that by applying the optimization process, in buildings with low height the reductions of CO2 emissions are greater than in high-rise buildings. Three metaheuristic algorithms consisting of Enhanced Colliding Bodies Optimization (ECBO), Enhanced Vibrating Particles System (EVPS) and Particle Swarm Optimization (PSO) have been used to solve the examples. A comparison of the performance of these algorithms shows that in optimization of RC structures, which has many design variables and a large search space, the ECBO algorithm has higher search capability and results in better solutions than EVPS and PSO algorithms.

关键词： Optimal cost Optimal CO2 emissions RC frames metaheuristic algorithms Enhanced colliding bodies optimization (ECBO)

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A new approach for health-oriented ozone control strategy: Adjoint-based optimization of NOx emission reductions using metaheuristic algorithms

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JOURNAL OF CLEANER PRODUCTION 2021年第20期312卷 127533-127533页

作者： Wang, Mengya Huang, Tao Wong, David C. Ho, Kin Fai Dong, Guanghui Yim, Steve H. L. Chinese Univ Hong Kong Dept Geog & Resource Management Sha Tin Hong Kong Peoples R China US EPA Ctr Environm Measurement & Modeling Atmospher & Environm Syst Modeling Div Res Triangle Pk NC USA Chinese Univ Hong Kong Jockey Club Sch Publ Hlth & Primary Care Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Inst Environm Energy & Sustainabil Sha Tin Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Key Lab Environm Pollut & Hlth Risk Ass Guangdong Prov Engn Technol Res Ctr Environm Poll Dept Prevent MedSch Publ Hlth Guangzhou 510080 Peoples R China Chinese Univ Hong Kong Stanley Ho Big Data Decis Analyt Res Ctr Shatin Hong Kong Peoples R China Nanyang Technol Univ Asian Sch Environm Singapore Singapore

While levels of particulate matters in the Pearl River Delta Region (PRD) show a significant reduction, ozone (O3) has an opposite increasing trend, becoming the critical air quality target in this decade. Emission control strategies are typically formulated sector by sector, spatial variability in emissions reductions and health impacts of air pollutants may not be taken into account, affecting the overall effectiveness of control strategies. This study proposes an adjoint-based optimization framework to facilitate health-oriented O3 control over PRD. The location-specific adjoint sensitivity coefficients, which reflect the spatiotemporal influences from emissions of nitrogen dioxide (NOx) on O3 health impacts, are combined with metaheuristic algorithms to minimize the O3related premature mortalities over receptor regions. Using the proposed optimization methodology, the regional O3 health benefits under current emission reduction policy can be increased by 16-27%. The results show that relatively larger NOx emissions reductions occurred at highly developed and populated areas. Particularly, significant reductions in NOx emissions are observed at Shenzhen and urban Guangzhou. Furthermore, implementing regional NOx emissions abatement has advantages to achieve an overall O3 health benefits for all cities. The interregional influences of NOx emissions abatement between cities indicate a promising strategy of healthoriented O3 control in PRD.

关键词： Ozone control strategy Adjoint sensitivity Ozone health benefits Emission optimization metaheuristic algorithms

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Optimization of a Transit Services Model with a Feeder Bus and Rail System Using metaheuristic algorithms

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2015年第6期29卷 04014090-04014090页

作者： Almasi, Mohammad Hadi Sadollah, Ali Mounes, Sina Mirzapour Karim, Mohamed Rehan Univ Malaya Fac Engn CTR Kuala Lumpur 50603 Malaysia Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

Nowadays, many passengers use transit systems to reach their destinations;however, the growing concern for public transit is its inability to shift passenger's mode from private to public transportation. By designing a well-integrated public transit system and improving the cost-effectiveness network, the public transport could play a crucial role in passenger satisfaction and reducing the operating cost. The main target of this paper is to present a new mathematical programming model and design an efficient transit system to increase the efficiency of integrated public transit services through the development of feeder bus services and coordination of major transportation services with the aim of minimizing the costs. In this study, optimized transit services and coordinated schedules are developed using metaheuristic algorithms such as genetic algorithm, particle swarm optimization, and imperialist competitive algorithm. The data used and the coordination were obtained from a case study widely provided in the literature. Finally, obtained numerical results of the proposed model including optimal solution, statistical optimization results, and the convergence rate, and comparisons are discussed in detail using tables and figures. (C) 2014 American Society of Civil Engineers.

关键词： Public transportation Transit network design Feeder bus Intermodal coordination Routing and scheduling metaheuristic algorithms

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A comparative analysis of metaheuristic algorithms for optimizing curved roof structures

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STRUCTURES 2024年 70卷

作者： Ustuner, Betul Aydogdu, Ibrahim Ozyurek, Canip Dogan, Erkan Manisa Celal Bayar Univ Dept Civil Engn Manisa Turkiye Akdeniz Univ Dept Civil Engn Dumlupinar Blvd TR-07058 Antalya Turkiye Minist Natl Def Republ Turkey Ankara Turkiye

This article explores the optimal design of curved steel structures with a focus on minimizing their weight by determining the most suitable cross-sections. Customized optimization algorithms were developed to identify structural designs that meet safety and durability requirements, adhering to design constraints set by the ASDAISC specification. To ensure reliable results, the study employs a variety of metaheuristic optimization methods: Biogeography-Based Optimization (BBO), Dynamic Harmony Search (DHS), Wolf Colony Algorithm (WCA), and Honey Badger Algorithm (HBA). The Honey Badger Algorithm, a relatively new addition to the field, was used for the first time in the context of structural optimization for curved roof systems. This unique approach aims to evaluate its performance in civil engineering problems and compare it with other established algorithms. The major challenges of this study lie in the inherent complexity of dome structures, which involve a large number of elements and nonlinear constraints. The subdivision of the structure into smaller groups was necessary to manage the computational load, although this introduced additional complexities. Despite these challenges, the metaheuristic methods demonstrated their robustness in addressing such intricate engineering problems. Additionally, data retrieval is facilitated through Open Application Interface (OAPI) functions, enabling seamless data transfer between SAP 2000 and Visual Basic. The study's final design example involves a dome model with 2556 elements, which was both modeled and optimized. The results demonstrate the effectiveness of these optimization algorithms in achieving structurally sound and efficient designs. The insights gained from this study contribute to our understanding of optimized cross-sections in curved steel structures, offering valuable guidance for improving structural performance and minimizing material usage.

关键词： Optimum structural design metaheuristic algorithms Swarm intelligence Honey Badger Algorithm Curved roof truss optimization OAPI

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Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using metaheuristic algorithms

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第4期65卷 1092-1113页

作者： Kaveh, Ali Dastjerdi, Mohammad Iman Karimi Zaerreza, Ataollah Hosseini, Milad Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

关键词： curved roof frames pitched roof frames tapered members discrete optimization metaheuristic algorithms

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A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 105卷 104410-104410页

作者： Kurban, Rifat Durmus, Ali Karakose, Ercan Kayseri Univ Vocat Sch Tech Sci Dept Comp Technol Kayseri Turkey Kayseri Univ Vocat Sch Tech Sci Dept Elect & Energy Kayseri Turkey Kayseri Univ Engn & Architecture & Design Fac Dept Nat Sci Kayseri Turkey

Color image thresholding is a well-known approach for image segmentation. An objective function, based on image entropy, is defined by threshold numbers and locations in the color histogram. Multiple classes of images can be created with multilevel thresholding. The main problem with thresholding techniques is to decide the threshold color values for each image. For a human operator, it is very hard to determine the specific threshold values of the images to be segmented. From this perspective, multilevel color image thresholding can be considered as an optimization problem that an algorithm should determine the optimum threshold values to obtain a perfectly segmented image. In recent years, metaheuristic algorithms become popular in several fields including image thresholding by their advantage of flexible structure. The motivation of this study relies on the fact that using same algorithm to solve any particular problem do not guarantee the best results as stated in no-free lunch theorem in optimization. Therefore, six novel nature inspired algorithms such as equilibrium optimization (EO), political optimizer (PO), turbulent flow of water-based optimization (TFWO), henry gas solubility optimization (HGSO), marine predators algorithm (MPA), and slime mould algorithm (SMA) are chosen to be compared by determining the multilevel color image thresholding values. These algorithms, which are used for the first time to solve this problem, are also compared statistically with extensive experiments. Aerial test images obtained by drones are used in the experiments. Kapur's entropy and between-class variance (Otsu's method) objectives are maximized by metaheuristic algorithms. Experimental results are evaluated with structural similarity (SSIM), peak-signal noise ratio (PSNR), blind/referenceless image spatial quality evaluator (BRISQUE), perception-based image quality evaluator (PIQE), natural image quality evaluator (NIQE), and computing CPU time consumption of the algori

关键词： Color image segmentation Multilevel thresholding metaheuristic algorithms Kapur's entropy Between class variance (Otsu's method)

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Edge optimized and personalized lifelogging framework using ensembled metaheuristic algorithms

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Agarwal, Preeti Alam, Mansaf Jamia Millia Islamia Dept Comp Sci New Delhi India

The fostered use of smart wearables for lifelogging daily activities has fuelled massive data generation. Lack of personalization, massive network traffic, increased latency, and high vulnerability to missing and noisy data are the significant impediments that existing frameworks face. This paper proposes a user-personalized and edge-optimized four-layer framework for lifelogging activities to address these impediments. A lightweight Edge Intelligence (EI) module with low computation requirements is designed to reduce data transmission to the cloud, lowering energy consumption. A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is proposed to provide a user-specific and optimized set of features. MSP optimized Decision Tree (MSP-DT) classifier is developed for real-time activity recognition in the Spark environment. The classifier's performance is calibrated regularly, making the framework resilient to sensor failure. Experiments demonstrate that the proposed framework can recognize 12 physical activities of different subjects with a mean accuracy of 97.67% and 47.66% reduction in transmitted data.

关键词： Internet of Things (IoT) Edge intelligence Big Data Analytics (BDA) metaheuristic algorithms Human Activity Recognition (HAR) Cloud Computing (CC) Edge Computing (EC)

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Advancing basalt fiber asphalt concrete design: A novel approach using gradient boosting and metaheuristic algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2023年 19卷

作者： Phung, Ba Nhan Le, Thanh-Hai Mai, Hai-Van Thi Nguyen, Thuy-Anh Ly, Hai -Bang Univ Transport Technol Hanoi 100000 Vietnam

Basalt Fiber Asphalt Concrete (BFAC) is an environmentally friendly and durable material with potential road, bridge, and infrastructure construction applications. This study investigates the application of Machine Learning (ML) models, specifically the classical Gradient Boosting (CGB) algorithm, in conjunction with metaheuristic algorithms, to predict the Marshall Stability (MS) and optimize the design of BFAC mixtures. The model is trained and tested on a comprehensive dataset of experimental samples, taking into account various input parameters, including basalt fiber (BF) properties, asphalt binder characteristics, and aggregate gradation. Hyperparameter tuning is employed to enhance the model's predictive performance using metaheuristic algorithms such as Particle Swarm Optimization (PSO), Hunger Games Search (HGS), and Bald Eagle Search (BES) and compared regarding the convergence and computational efficiency. The findings demonstrate that BES outperforms other algorithms, achieving the highest performance. The CGB-BES model is then applied to three optimization scenarios, focusing on maximizing the MS while minimizing BF and asphalt binder content. Post-processing and interpretation of the results reveal the importance of combining ML and materials engineering expertise. By highlighting the synergy between CGB-BES model and domain-specific knowledge, materials engineers can effectively optimize the mixtures and improve the design and performance of BFAC.

关键词： Marshall stability Basalt fiber Asphalt concrete Machine Learning metaheuristic algorithms

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