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Optimizing pile bearing capacity prediction: Insights from dynamic testing and smart algorithms in geotechnical engineering

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MEASUREMENT 2024年 230卷

作者： Fattahi, Hadi Ghaedi, Hossein Malekmahmoodi, Farshad Armaghani, Danial Jahed Arak Univ Technol Fac Earth Sci Engn Arak Iran Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

In contemporary mining and geotechnical projects, various approaches are employed to predict the bearing capacity of piles (Qu). However, accurately modeling pile behavior using numerical, experimental, analytical, and regression methods proves challenging and, at times, infeasible due to the intricate nature of geotechnical materials, uncertainties, and the interaction between soil and piles. Consequently, formulations are generally presented, incorporating assumptions and simplifications that deviate from the actual complexity of the problem. Moreover, despite the high accuracy of pile loading tests as a reliable method in various design stages, their high costs and time requirements deter designers from conducting field tests. To address these challenges, this study performed 50 dynamic tests (HSDT) on precast concrete piles in Indonesia, Pekanbaru, to generate the necessary datasets. To mitigate experimental costs, two optimization algorithms fruit fly optimization (FFO) and invasive weed optimization (IWO) were employed. These models incorporated pile set (S), drop height (H), hammer weight (W), cross-sectional area (A), and length (L) as input parameters. Finally, the models' accuracy was evaluated using squared correlation coefficient (R2), variance accounted for (VAF), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results revealed that the FFO algorithm achieved an accuracy range of 0.971-0.978. Similarly, the IWO algorithm exhibited an accuracy range of 0.984-0.988. Additionally, sensitivity analysis indicated that, among the input parameters, W had the most significant impact on the Qu in both algorithms.

关键词： Pile bearing capacity Fruit fly optimization algorithm invasive weed optimization algorithm Sensitivity analysis Dynamic tests

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Developing drilling rate index prediction: A comparative study of RVR-IWO and RVR-SFL models for rock excavation projects

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GEOMECHANICS AND ENGINEERING 2024年第2期36卷 111-119页

作者： Fattahi, Hadi Bayat, Nasim Arak Univ Technol Fac Earth Sci Engn Arak Iran

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the invasive weed optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the invasive weed optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R2) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of dril

关键词： drilling rate index invasive weed optimization algorithm relevance vector regression rock geomechanical properties shuffled frog leaping algorithm

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Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

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WATER RESOURCES MANAGEMENT 2022年第9期36卷 3181-3205页

作者： Moghadam, Seyedeh Hadis Ashofteh, Parisa-Sadat Loaiciga, Hugo A. Univ Qom Dept Civil Engn Qom Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93016 USA

The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in conjunctive (surface water/groundwater) systems for water supply in agricultural municipal/industrial (M&I) sectors under climate change. Climatic variables are simulated with atmospheric-ocean general circulation models (AOGCMs) under emission scenarios A2 and B2 during the baseline period 1971-2000 and the future periods 2040-2069 and 2070-2099 in the Khorramabad basin, Iran. The Hadley Centre Coupled Model, version 3 (HadCM3), and the Canadian Global Coupled Model, version 2 (CGCM2), produced superior temperature and rainfall projections, respectively, than other climate models. Under both emissions scenarios and during each future period, this study indicates an increase in temperature and a decrease in rainfall. Simulations of surface water with the IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) calibrated model shows a decrease in the future runoff. The Groundwater Modeling System (GMS) calibrated software projects a decrease in water level and a decrease in recharge under climate change scenarios. Simulation results from IHACRES and GMS are input to the Water Evaluation and Planning (WEAP) system to develop operational policies for the combined use of water resources., The water-allocation reliability of the system is estimated with the WEAP system for 24 scenarios reflecting climate change scenarios assuming increases in water demand, ranging from 10 to 60% in agriculture and from 20 to 30% in the municipal and industrial (M&I) sector. The IWOA is applied to optimize the conjunctive system of water resources (i.e., surface water and groundwater). The objective function is to maximize the system's water allocation reliability. The range of optimal water-allocation reliability changes is between 3 and 16%, with the lowest increase

关键词： Climate change Optimal operating of surface water and groundwater conjunctive systems invasive weed optimization algorithm

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New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity

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SOFT COMPUTING 2021年第21期25卷 13451-13464页

作者： Emamgholizadeh, Samad Mohammadi, Babak Shahrood Univ Technol Dept Water & Environm Engn Fac Civil Engn Shahrood Iran Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden

Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R-2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R-2) of 0.229 Cmol + kg(-1) (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R-2) of 0.335 Cmol + kg(-1) (0.843) and 0.279 Cmol + kg(-1) (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.

关键词： Soil cation exchange capacity Soil physics Support vector machine Particle swarm optimization invasive weed optimization algorithm

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invasive weed Optimized Area Centralized 2 Degree of Freedom Combined PID Controller Scheme for Automatic Generation Control

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第1期16卷 31-42页

作者： Azaharahmed, Mohammed Raja, K. Patan, Muzeeb Khan Prasad, Ch. Durga Ganeshan, P. Anna Univ Coll Engn Dindigul Dindigul 624622 Tamil Nadu India Natl Inst Technol Raipur Dept Elect Engn Raipur 492010 Madhya Pradesh India Sethu Inst Technol Virudunagar 626115 Tamil Nadu India

Frequency disturbances caused by load perturbations of multi area interconnected power system are reduced by using proper control schemes provided via secondary control loop along with primary control of generation plants. These secondary controllers provide simultaneous control signal to individual machines of a particular area when the control scheme is area centralized. This area centralized mechanism provides quick control action with less computational burden since control problem dimensions gets reduced even the area consists multiple plants/machines. Such centralized new cascade control scheme is proposed in this paper to supervise the secondary control mechanism and it is implemented with parallel connection of 2-Degree of Freedom Proportional-Integral-Derivative (2-DOF PID) controller combined with regular PID controller. The performance of this new controller is studied on multi area multi machine interconnected power system with participation factor concept and later its relative performance in terms of dynamic and steady state specifications are compared with conventional PID and 2-DOF PID controllers. These control parameters are tuned by invasive weed optimization (IWO) algorithm to achieve better system outputs. Case studies presented in this paper show the advantages of proposed control scheme.

关键词： Automatic generation control (AGC) 2-DOF-PID controller invasive weed optimization algorithm

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A Novel Industrial Image Contrast Enhancement Technique Based on an Improved Ant Lion Optimizer

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第4期46卷 3235-3246页

作者： Yue, Xiaofeng Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

This work introduces an improved ant lion optimizer (ALO), called BIALO, for industrial images. BIALO employs three strategies to improve the performance of the original ALO. First, a novel inertial weight is used to modify the ALO to better balance exploration and exploitation during the process of searching the best solutions. Second, the local search part of the bat algorithm plays an important role in accelerating the algorithm convergence rate. Additionally, the ALO is integrated with invasive weed optimization algorithm to further improve the searching precision. The proposed BIALO is applied to industrial image enhancement, where it acts as an efficient tool that searches for the best parameters in a local/global enhancement transformation. To test the performance of BIALO, we compare it with other metaheuristic algorithms, such as the genetic algorithm, particle swarm optimization, flower pollination algorithm, grasshopper optimization algorithm and the original ALO, on some benchmark industrial images. The experimental results establish that BIALO is able to achieve better outcomes than those of the other algorithms.

关键词： Industrial image contrast enhancement Ant lion optimizer Novel inertial weight Bat algorithm invasive weed optimization algorithm

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Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期18卷 313-328页

作者： Pahlavani, Parham Delavar, Mahmoud R. Frank, Andrew U. Univ Tehran Coll Eng Dept Surveying & Geomat Eng Ctr Excellence Geomat Eng Disaster Management Tehran Iran Vienna Univ Technol Inst Geoinformat & Cartog A-1040 Vienna Austria

The personalized urban multi-criteria quasi-optimum path problem (PUMQPP) is a branch of multi-criteria shortest path problems (MSPPs) and it is classified as a NP-hard problem. To solve the PUMQPP, by considering dependent criteria in route selection, there is a need for approaches that achieve the best compromise of possible solutions/routes. Recently, invasive weed optimization (IWO) algorithm is introduced and used as a novel algorithm to solve many continuous optimization problems. In this study, the modified algorithm of IWO was designed, implemented, evaluated, and compared with the genetic algorithm (GA) to solve the PUMQPP in a directed urban transportation network. In comparison with the GA, the results have shown the significant superiority of the proposed modified IWO algorithm in exploring a discrete search-space of the urban transportation network. In this regard, the proposed modified IWO algorithm has reached better results in fitness function, quality metric and running-time values in comparison with those of the GA. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Personalized urban multi-criteria path optimization problem invasive weed optimization algorithm Genetic algorithm

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Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期109卷 104647-104647页

作者： Nakkala, Mallikarjun Rao Singh, Alok Rossi, Andre Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India Univ Paris Dauphine PSL LAMSADE UMR CNRS 7243 Pl Marechal Lattre Tassigny F-75775 Paris 16 France

There exist numerous heuristic and exact approaches in the literature for addressing minimum weight dominating set problem (MWDS) on vertex-weighted undirected graphs which is a well known NP-hard problem. However, little attention has been paid to its counterpart in vertex-weighted directed graphs called minimum weight directed dominating set problem (MWDDS) despite its use in modeling real-world applications involving directed interactions. As directed graphs can model undirected graphs, MWDDS can be considered as a generalization of MWDS, and hence, MWDDS is also NP-hard. In this paper, we present two approaches based on swarm intelligence, one approach based on integer linear programming (ILP) and one matheuristic approach to address the MWDDS. These approaches are the first approaches for MWDDS in their respective categories. One of our swarm intelligence approach is based on artificial be colony (ABC) algorithm, whereas the other is based on invasive weed optimization (IWO) algorithm. Both these approaches are hybridized with problem specific heuristics and a local search mechanism. We have evaluated the performance of our approaches on benchmark instances derived from the standard benchmark instances of MWDS. Computational results show the effectiveness of our approaches.

关键词： Directed dominating set Minimum weight directed dominating set Artificial bee colony algorithm invasive weed optimization algorithm Swarm intelligence Matheuristic

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Joint decision on product greenness strategies and pricing in a dual-channel supply chain: A robust possibilistic approach

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JOURNAL OF CLEANER PRODUCTION 2020年 256卷 120437-120437页

作者： Rahmani, Donya Abadi, Moein Qaisari Hasan Hosseininezhad, Seyed Javad KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Nowadays, by increasing the sensitivity of governments to the issue of the environment, green supply chain design has become very significant. This paper presents a mathematical model based on a dual-channel system for designing a green supply chain. The first sales channel is based on the traditional retail purchasing system, while in the second channel the customers purchase the products from the plants directly. In the proposed model, decisions on location, products transfer and pricing are determined. Furthermore, the decision on the kind of manufacturing technology in each plant to be established is considered as well. Any kind of technology leads to a specific level of the greenness of the products in each plant. A product with a higher green level has a lower greenhouse gas emission but higher costs. In order to encourage managers to produce green products, government subsidy policies are in place. Besides, because of the uncertain nature of some parameters, a robust possibilistic optimization approach has been used. As the proposed mixed integer nonlinear model is complicated to solve, an invasive weed optimization algorithm (IWO) is proposed to obtain efficient solutions. Due to the nonlinear type of the developed model and due to there is no benchmark available in the related literature to validate the results, Cultural algorithm (CA) and Genetic algorithm (GA) are utilized. The results indicate that IWO performs better than others. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Dual-channel supply chain Pricing invasive weed optimization algorithm Government subsidy Robust possibilistic optimization

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Applying improved artificial neural network models to evaluate drilling rate index

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2017年第Nov.期70卷 114-124页

作者： Fattahi, Hadi Bazdar, Habibollah Arak Univ Technol Dept Min Engn Arak *** Iran

The drilling rate index (DRI) is the most important input parameter of a commonly used performance prediction model for drilling and rock excavation. In this paper, the hybrid artificial neural network (ANN) with back propagation (BP) algorithm, simulated annealing algorithm (SAA), firefly algorithm (FA), invasive weed optimization algorithm (IWO) and shuffled frog leaping algorithm (SFLA) were used to build a prediction model for the indirect estimation of DRI. The estimation abilities offered using five ANN models (ANN-BP, ANN-SAA, ANN FA, ANN-IWO and ANN-SFLA) were presented by using available data given in open source literature. In these models, strengths (Uniaxial Compressive Strength (UCS) and Brazilian Tensile Strength (BTS)) and indexes properties (Shore Scleroscope Hardness (SSH), diametral point load strength index (Is((50)) ->) and axial point load strength index (Is(sol)) were utilized as the input parameters, while the DRI was the output parameter. Various statistical performance indexes were utilized to compare the performance of those estimation models. The comparative results revealed that hybrid of SAA and ANN yield robust model which outperform other models in term of higher squared correlation coefficient (R-2), variance account for (VAF) and lower mean square error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE).

关键词： Drilling rate index Artificial neural network Simulated annealing algorithm Firefly algorithm invasive weed optimization algorithm Shuffled frog leaping algorithm

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