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The determination of the strongest attributes of high-performance concrete featuring innovative admixtures via optimal regression-based methodologies

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 883-893页

作者： Zhang, Lianchao Guo, Shizhong Qi, Jianwei Tangshan Maritime Inst Tangshan 063000 Hebei Peoples R China

The acquisition of an optimal mix design for high-performance concrete (HPC) is known to present significant challenges due to its inherently intricate nature. Therefore, implementing machine learning techniques can effectively streamline the process, resulting in more efficient use of resources and a reduction in labor intensity. The present study has employed the support vector regression (SVR) methodology to forecast the compressive strength (CS) of HPC. In addition, the present investigation furnished two meta-heuristic techniques for attaining appropriate and suitable outcomes. Specifically, these techniques encompass the artificial hummingbird algorithm (AHA) and flow direction algorithm (FDA). The hybridized approach within the SVR-AHA and SVR-FDA frameworks was achieved by integrating algorithms with the model. Moreover, several criteria indicators have been employed to identify the optimal hybrid model. About 170 experimental data sets have been used to validate the performance of the developed method, which the samples split into two stages, including training and testing. Consequently, the utilization of the AHA algorithm in conjunction with the SVR model led to an enhanced association model, as evidenced by the following outcomes: RMSE = 2.00 (MPa), R2 = 98.59%, WAPE = 0.114 (MPa), MAPE = 1.22 (MPa), and. MAE = 0.717 (MPa).

关键词： High-performance concrete flow direction algorithm Artificial hummingbird algorithm Support vector regression Compressive strength

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Prediction of California bearing ratio using multi-layer perceptron model based on multiple meta-heuristic optimizers

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2489-2505页

作者： Chen, Jianhong Chengdu Polytech Coll Urban Construct Chengdu 610041 Sichuan Peoples R China

The California bearing ratio (CBR) is commonly employed within the field of geotechnical engineering and its associated applications, including but not limited to highway embankments, earth dams, and bridge abutments. This index can typically be ascertained through laboratory analyses or field test procedures. Nonetheless, the method of determining CBR is both time- and cost-intensive. Consequently, this research endeavors to present three hybrid models that integrate the multi-layer perceptron model (MLP) with three meta-heuristic algorithms, namely, the flow direction algorithm (FDA), Prairie dog optimization (PDO), and red deer algorithm (RDA). These models offer a cost-effective and efficient methodology for predicting CBR values while concurrently exhibiting enhanced precision in dealing with real-world complexities. The present study determined that 70% of the developed hybrid models were assigned to the training phase, while the 30% remaining were designated testing models. This study assessed the influence of eight distinct factors on the anticipated CBR values. To assess the precision of the models developed for two categories, an analysis is conducted that involves a comparative examination of projected outcomes versus observed results. This evaluation utilizes five distinct statistical measures, namely, the coefficient of determination (R2), root mean squared error (RMSE), mean squared error (MSE), mean absolute relative error (MARE), and uncertainty (U95%).

关键词： California bearing ratio Multi-layer perceptron model Prairie dog optimization flow direction algorithm Red deer algorithm

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A FDA-based multi-robot cooperation algorithm for multi-target searching in unknown environments

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 7741-7764页

作者： Ye, Wenwen Cai, Jia Li, Shengping Zhaoqing Univ Sch Math & Stat Zhaoqing St Zhaoqing 526061 Guangdong Peoples R China Guangdong Univ Finance & Econ Sch Digital Econ Guangzhou 510320 Guangdong Peoples R China Shantou Univ Dept Mech Engn Tuojiang St Shantou 535063 Guangdong Peoples R China Shantou Univ MOE Key Lab Intelligent Mfg Tuojiang St Shantou 535063 Guangdong Peoples R China

Target search using a swarm of robots is a classic research topic that poses challenges, particularly in conducting multi-target searching in unknown environments. Key challenges include high communication cost among robots, unknown positions of obstacles, and the presence of multiple targets. To address these challenges, we propose a novel Robotic flow direction algorithm (RFDA), building upon the modified flow direction algorithm (FDA) to suit the characteristics of the robot's motion. RFDA efficiently reduces the communication cost and navigates around unknown obstacles. The algorithm also accounts for scenarios involving isolated robots. The pipeline of the proposed RFDA method is outlined as follows: (1). Learning strategy: a neighborhood information based learning strategy is adopted to enhance the FDA's position update formula. This allows swarm robots to systematically locate the target (the lowest height) in a stepwise manner. (2). Adaptive inertia weighting: An adaptive inertia weighting mechanism is employed to maintain diversity among robots during the search and avoid premature convergence. (3). Sink-filling process: The algorithm simulates the sink-filling process and moving to the aspect slope to escape from local optima. (4). Isolated robot scenario: The case of an isolated robot (a robot without neighbors) is considered. Global optimal information is only required when the robot is isolated or undergoing the sink-filling process, thereby reducing communication costs. We not only demonstrate the probabilistic completeness of RFDA but also validate its effectiveness by comparing it with six other competing algorithms in a simulated environment. Experiments cover various aspects such as target number, population size, and environment size. Our findings indicate that RFDA outperforms other methods in terms of the number of required iterations and the full success rate. The Friedman and Wilcoxon tests further demonstrate the superiority of RFDA.

关键词： flow direction algorithm Swarm robot Multi-target searching Neighborhood information based learning strategy

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Predicting the settlement of pile based on a hybrid form of the model by considering Least Square Support Vector Regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 529-542页

作者： Chen, Qiang Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

Understanding the vertical settlement is crucial when designing the pile and foundation type used in real estate, specifically regarding the pile settlement (Sp). This issue is of utmost importance due to the numerous variables involved in designing piles that penetrate rock. Despite multiple efforts, clear and precise theoretical explanations regarding the interactions between soil and piles are currently unclear. As a result, many studies have opted to employ artificial intelligence techniques for determining the subsidence rate of piles over time under different loading conditions. This study presents a machine learning (ML) that effectively predicts the values of Sp, namely Least Square Support Vector Regression (LSSVR). In addition, the proposed model coupled with three meta-heuristic algorithms, including the flow direction algorithm (FDA), Chimp Optimization algorithm (ChOA), and Rider Optimization algorithm (ROA), to improve the performance and obtain the optimal results as a framework of hybrid. As a result, LSFD determined the most suitable effects with R2 and RMSE values equal to 0.2503 and 0.9952, respectively. Overall, using LSSVR with FDA, ChOA, and ROA can improve the accuracy and robustness of the model in predicting pile settlement, making it a valuable tool for geotechnical engineers designing foundation systems.

关键词： Pile settlement Least Square support vector regression flow direction algorithm Chimp optimization algorithm Rider optimization algorithm

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An Improved D8-LTD for the Extraction of Total Contributing Area (TCA) by Adopting the Strategies of Path Independency and Local Dispersion

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WATER RESOURCES RESEARCH 2022年第2期58卷 e2021WR030948-e2021WR030948页

作者： Li, Zhenya Shi, Pengfei Yang, Tao Wang, Chao Yong, Bin Song, Ying Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Ctr Global Change & Water Cycle Nanjing Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Peoples R China Yangtze Inst Conservat & Dev Nanjing Peoples R China New Mexico Inst Min & Technol Dept Earth & Environm Sci Hydrol Program Socorro NM 87801 USA

D8-LTD is a path-based flow direction algorithm for estimating total contributing area (TCA). However, the precision of D8-LTD TCA is seriously challenged by the incorporation of flow paths and basic hypothesis of allowing only one receiving pixel. To solve above problems, this work proposes an improved D8-LTD by adopting the strategies of path independency and local dispersion (i.e., LTD-PI-LD). Two indicators are adopted to quantify size and extent precisions of estimated TCAs relative to theoretical TCA. Then, LTD-PI-LD is compared with several typical single-flow-direction (SFD) algorithms (i.e., D8, D8-LTD, LTD-PI) and multiple-flow-direction (MFD) algorithms (i.e., MFD-Quinn, D infinity) on synthetic surfaces. Results indicate that (1) TCA maps of D8 and D8-LTD contain too many singular high-TCA tubes and source pixels, which are caused by the incorporation of flow paths and converging characteristics of SFD algorithms. (2) In MFD algorithms, the problem of artificial dispersion triggers the mechanisms of cross compensation and mutual amplification, leading to high size precision but low extent precision in estimated TCAs. (3) Strategy of path independency prevents the occurrence of high-TCA tubes and effectively reduces the number of source pixels. (4) Both size and extent precisions of D8-LTD TCAs are largely improved by introducing the strategies of path independency and local dispersion. Of all algorithms, LTD-PI-LD obtains the highest/second-highest size precision and the highest extent precision in estimated TCAs. As a whole, LTD-PI-LD shows advantages of high precision in estimated TCAs, low computation cost, high efficiency and robustness to DEM (Digital elevation model) resolution and terrain type.

关键词： flow direction algorithm total contributing area improved D8-LTD path independency local dispersion

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An algorithm-Independent Definition of Effective Contour Length and Its Impacts on Pixel-Scale Specific Catchment Area

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WATER RESOURCES RESEARCH 2023年第8期59卷 e2022WR034036-e2022WR034036页

作者： Li, Zhenya Lai, Xijun Shi, Pengfei Yang, Tao Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing Peoples R China

Specific catchment area (SCA) is crucial terrain attribute extensively-adopted in topographically-driven modelings, which is function of total contributing area (TCA) and effective contour length (EL). Despite of numerous studies about SCA and TCA, none has focused on the investigation of existing EL definitions and adaption of point-scale SCA concept to two-dimensional pixel of digital elevation model (DEM). This work conducts a systematic research on rationality of existing EL definitions, proposal of new EL definition and concept of pixel-scale SCA on gridded DEM. To overcome the defects of existing EL definitions, a new EL definition based on Triangular Discretization and Spatial Geometry (TDSG) is proposed in this work. SCA concept is extended from point scale to pixel scale for adapting to context of gridded DEM. The solutions of true pixel-scale SCA, TCA, and EL in integral forms are deduced based on the solutions of point-scale SCAs obtained by method of Zhou and Liu (2002, ) or Gallant and Hutchinson (2011, ). Besides, true pixel-scale SCA, TCA, and EL are solved analytically for several synthetic surfaces. Numerical experiments indicate that (a) TDSG definition is superior to all existing EL definitions in physical mechanism, spatial characteristics, and computational precisions on synthetic surfaces and a real-world DEM. (b) Spatial characteristics of SCA are controlled mainly by TCA and tuned slightly by EL, while overall precision of SCA is affected seriously by both TCA and EL. (c) TDSG definition is the only EL definition of responding accurately and sensitively to convergent and dispersive characteristics of local terrains.

关键词： effective contour length (EL) specific catchment area analytical solution EL definition flow direction algorithm

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FD-DBN: flow directed deep belief network for accurate anomaly detection in cloud computing

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第16期36卷 e5592-e5592页

作者： Rajarao, Banavathu Sreenivasulu, Meruva Jawaharlal Nehru Technol Univ Anantapur Dept Comp Sci & Engn Ananthapuramu Andhra Pradesh India JNTUA Ananthapuramu KSRM Coll Engn Dept Comp Sci & Engn Kadapa Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur Ananthapuramu 515002 Andhra Pradesh India

Security becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD-DBN), involves three main phases: pre-processing, clustering, and classification. In pre-processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game-based k-means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD-DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.

关键词： classification clustering data mining deep belief network deep learning flow direction algorithm intrusion detection system

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Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty

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ENERGY 2023年第PartA期278卷

作者： Boroumandfar, Gholamreza Khajehzadeh, Alimorad Eslami, Mahdiyeh Syah, Rahmad B. Y. Islamic Azad Univ Dept Elect Engn Kerman Branch Kerman Iran Univ Medan Area Fac Engn CoE PUIN Medan Indonesia

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very

关键词： Distribution network Hybrid PV WT Battery system Robust planning Information gap decision theory flow direction algorithm

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A Method for Dynamical Sub-Watershed Delimitating by No-Fill Digital Elevation Model and Defined Precipitation: A Case Study of Wuhan, China

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WATER 2020年第2期12卷 486-486页

作者： Zhang, Hongping Cheng, Xinwen Jin, Lei Zhao, Dong Feng, Tianjing Zheng, Kun China Univ Geosci Fac Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Engn Sci Fac Informat Engn Wuhan 430200 Peoples R China Yangtze Univ Coll Geophys & Petr Resources Wuhan 434023 Peoples R China

Watershed delimitation is very important in flood control management. The traditional sub-watersheds delimitated by a filling digital elevation model (DEM) may change the real sink area, such that it may not be the best choice in studies sensitive to sub-watershed storage. This paper proposes a dynamical watershed delimitation method using a no-fill DEM and precipitation. it considers a closed sink area containing cells that fully flow into a large special cell, which can flow out when its water level is "higher than outlet". We took Wuhan City as a study area and defined the precipitation in return periods of 1, 5, 20, or 100 years to derive the sub-watersheds. it is found that, in the four delimitations, the ratio of isolated basic units which could not flow outside were 27%, 9%, 5%, and 1%, respectively, as the precipitation increased. The results show that the provided method satisfies the assumption that the sink area might overflow with increased precipitation. The sub-watershed delimitated by the proposed method has higher correlation with the distribution of waterlogging points than those delimitated according to the D8 algorithm. These findings indicate that the proposed method can derive reasonable sub-watershed delimitation and that it may be helpful in the practice of urban flood control management.

关键词： urban flood source flood watershed delimitation sink area flow direction algorithm

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