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Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures

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HELIYON 2024年第12期10卷 e32856页

作者： Bin Inqiad, Waleed Dumitrascu, Elena Valentina Dobre, Robert Alexandru Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Natl Univ Sci & Technol Politehn Bucharest Fac Automat Control & Comp Sci Comp Dept Bucharest Romania Natl Univ Sci & Technol Politehn Bucharest Elect Technol & Reliabil Dept Bucharest 061071 Romania

The use of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) has escalated recently due to its significant advantages in contrast to normal concrete such as increased ductility, crack resistance, and eliminating the need for compaction etc. The process of determining residual strength properties of HFR-SCC after a fire event requires rigorous experimental work and extensive resources. Thus, this study presents a novel approach to develop equations for reliable prediction of compressive strength (cs) and flexural strength (fs) of HFR-SCC using gene expression programming (GEP) algorithm. The models were developed using data obtained from internationally published literature having eight inputs including water-cement ratio, temperature, fibre content etc. and two output parameters i.e., cs and fs. Also, different statistical error metrices like mean absolute error (MAE), coefficient of determination (R2) and objective function (OF) etc. were employed to assess the accuracy of developed equations. The error evaluation and external validation both approved the suitability of developed models to predict residual strengths. Also, sensitivity analysis was performed on the equations which revealed that temperature, water-cement ratio, and superplasticizer are some of the main contributors to predict residual compressive and flexural strength.

关键词： Residual compressive strength Flexural strength Fibres gene expression programming Fly ash Slag Elevated temperature

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Effective Mining of Contrast Hybrid Patterns from Nominal-numerical Mixed Data 18th

Effective Mining of Contrast Hybrid Patterns from Nominal-nu...

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18th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Fu, Min Duan, Lei Yu, Zhenyang Sichuan Univ Sch Comp Sci Chengdu Peoples R China Sichuan Univ Med X Ctr Informat Chengdu Peoples R China

ISBN: (纸本)9783031220630;9783031220647

Contrast pattern mining, which finds patterns describing differences between two classes of data, is an important task in various scenarios. As real-world data is usually a mixture of nominal and numerical attributes (e.g., electronic medical records), contrast pattern mining algorithms over nominal-numerical mixed data are in great demand. Existing algorithms on contrast pattern mining either can only handle a single type of attribute or transform numerical attributes into nominal attributes with prior knowledge. However, these algorithms may result in limited discrimination of contrast patterns due to the failure to exploit the original data information and inflexible pattern forms. In this paper, we propose a novel algorithm, CHPMiner, which mines a new kind of contrast pattern called contrast hybrid pattern (CHP) that contains nominal attributes and numerical relationships among numerical attributes based on extended gene expression programming (GEP). Specifically, CHPMiner develops two sub-expressions and a novel structure to combine nominal and numerical attributes. Moreover, CHPMiner leverages a specific fitness function to guide the evolution direction for mining CHPs that are highly discriminating. Experiments on four realworld datasets show that CHPMiner outperforms baselines. The case study further demonstrates the effectiveness of CHPMiner.

关键词： Contrast pattern mining Contrast hybrid pattern gene expression programming

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A comparative study between dynamic and soft computing models for sediment forecasting

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SOFT COMPUTING 2021年第16期25卷 11005-11017页

作者： Meshram, Sarita Gajbhiye Pourghasemi, Hamid Reza Abba, S., I Alvandi, Ehsan Meshram, Chandrashekhar Khedher, Khaled Mohamed Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Baze Univ Fac Engn Dept Civil Engn Abuja Nigeria Gorgan Univ Agr Sci & Nat Resources Dept Watershed Management Gorgan Golestan Iran Chhindwara Univ Dept Post Grad Studies & Res Math Jayawanti Haksar Govt Post Grad Coll Betul India King Khalid Univ Coll Engn Dept Civil Engn Abha Saudi Arabia High Inst Technol Studies Dept Civil Engn Mrezgua Univ Campus Nabeul 8000 Tunisia

Runoff-sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall-runoff-sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily-sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge-sediment data for 25 years were obtained from Shakkar Watershed;Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash-Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure.

关键词： Sediment yield Runoff Dynamic model ANN SVM gene expression programming

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An integrated data mining approach to predict electrical energy consumption

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第3期17卷 142-153页

作者： Fallahpour, Alireza Barri, Kaveh Wong, Kuan Yew Jiao, Pengcheng Alavi, Amir H. Univ Teknol Malaysia Sch Mech Engn Skudai 81310 Malaysia Univ Pittsburgh Dept Civil & Environm Engn Pittsburgh PA 15260 USA Zhejiang Univ Inst Port Coastal & Offshore Engn Ocean Coll Zhoushan 316021 Zhejiang Peoples R China Zhejiang Univ Engn Res Ctr Ocean Sensing Technol & Equipment Minist Educ Zhoushan Peoples R China Univ Pittsburgh Dept Bioengn Pittsburgh PA 15260 USA Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

This study proposes an integrated adaptive neuro fuzzy inference system (ANFIS) and gene expression programming (GEP) approach to predict long-term electrical energy consumption. The developed hybrid method uses ANFIS to find parameters with maximum effect on the electricity demand. Thereafter, the GEP algorithm is deployed to derive a robust mathematical model for the prediction of the electricity demand. Various statistical criteria are considered to verify the validity of the model. The predictions made by the ANFIS-GEP model are compared with those obtained by the simple GEP and hybrid artificial neural network (ANN)-ANFIS methods. The proposed ANFIS-GEP technique is more computationally efficient and accurate than GEP, and notably outperforms ANFIS-ANN.

关键词： electricity demand forecasting feature selection ANFIS gene expression programming GEP formulation

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A hybrid intelligent model for acute hypotensive episode prediction with large-scale data

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INFORMATION SCIENCES 2021年 546卷 787-802页

作者： Jiang, Dazhi Tu, Geng Jin, Donghui Wu, Kaichao Liu, Cheng Zheng, Lin Zhou, Teng Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou 515063 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Sch Nursing Hong Kong 000977 Peoples R China

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer insights to neuroscientists, biologists, and even provide support for clinicians. This paper presents a hybrid artificial intelligence model combined with CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise, a typical method for physiological signal decomposition), deep learning, multiple gene expression programming and fuzzy expert system for AHE detection. In this paper, the physiological signal is selected from a benchmark dataset, for example MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care II), which collects large scale real patients' data for clinical research. In the hybrid model, a typical signal decomposition method is employed for AHE signal processing, and an autoencoder based deep neural network is established for feature extraction. Finally, a reliable and explainable classifier is presented by fusing gene expression programming and the fuzzy method. Experimental results based on real data set demonstrate that the proposed method outperforms stateof-the-art AHE detection methods by achieving the prediction accuracy of 88.14% in 2866 records. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Acute hypotensive episode Artificial intelligence model Deep learning gene expression programming Fuzzy system

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Smart learning strategy for predicting viscoelastic surfactant (VES) viscosity in oil well matrix acidizing process using a rigorous mathematical approach

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SN APPLIED SCIENCES 2021年第10期3卷 815页

作者： Mahdaviara, Mehdi Rostami, Alireza Shahbazi, Khalil Amirkabir Univ Technol AUT Dept Petr Engn Tehran Iran Petr Univ Technol PUT Dept Petr Engn Ahvaz Iran

This piece of study attempts to accurately anticipate the apparent viscosity of the viscoelastic surfactant (VES) based self-diverting acids as a function of VES concentration, temperature, shear rate, and pH value. The focus not only is on generating computer-aided models but also on developing a straightforward and reliable explicit mathematical expression. Towards this end, gene expression programming (GEP) is used to connect the aforementioned features to and the target. The GEP network is trained using a wide dataset adopted from open literature and leads to an empirical correlation for fulfilling the aim of this study. The performance of the proposed model is shown to be fair enough. The accuracy analysis indicates satisfactory Root Mean Square Error and R-squared values of 7.07 and 0.95, respectively. Additionally, the proposed GEP model is compared with literature published correlations and established itself as the superior approach for predicting the viscosity of VES-based acids. Accordingly, the GEP model can be potentially served as an efficient alternative to experimental measurements. Its obvious advantages are saving time, lowering the expenses, avoiding sophisticated experimental procedures, and accelerating the diverter design in stimulation operations.

关键词： Viscoelastic surfactant Artificial intelligence gene expression programming Matrix acidizing Carbonate rock

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Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling

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CHEMOSPHERE 2023年 313卷 137336页

作者： Yaqub, Muhammad Nguyen, Mai Ngoc Lee, Wontae Kumoh Natl Inst Technol Dept Environm Engn 61 Daehak Ro Gumi 39177 South Korea

Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (133Cs+) and natural organic matter (NOM) from an aqueous solution. The Cs+ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs+ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs + adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs+ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs+ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R2 values of 0.9348 and 0.889, respectively.

关键词： Adsorption Cesium gene expression programming Heated aluminum oxide particles Natural organic matter Prussian blue

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Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104183-104183页

作者： Meng, Kai Tang, Qiuhua Zhang, Zikai Yu, Chunlong Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm;the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules;the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic.

关键词： Assembly line balancing Preventive maintenance Heuristic rule Grey wolf optimizer algorithm gene expression programming

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Mathematical model and augmented simulated annealing algorithm for mixed-model assembly job shop scheduling problem with batch transfer

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KNOWLEDGE-BASED SYSTEMS 2023年 279卷

作者： Cheng, Lixin Tang, Qiuhua Liu, Shengli Zhang, Liping Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China

In the mixed-model assembly job-shops, all the components are transferred in batches from the processing stage to the assembly stage to reduce transportation costs and timely satisfy the assembly material requirements. However, batch transfer in such a workshop has not received due attention in the current literature. Thus, this work addresses the mixed-model assembly job-shop scheduling problem with batch transfer via a mathematical model and an augmented simulated annealing algorithm to minimize manufacturing and transportation costs. The mathematical model takes batch transfer constraints into account and constructs the temporal and spatial links among the processing, transfer, and assembly stages. Production sequencing knowledge and batch transfer knowledge are first extracted via gene expression programming algorithm and clustering method and then implanted into the algorithmic operators in the form of rules and strategies to directly identify the promising solution space. Besides, eleven neighborhood structures including the critical component-based and batch based ones, are designed to enhance the exploitation ability. A restart mechanism considering crossover and tempering helps to get out of local optima. Experimental results indicate that by adopting the extracted knowledge, problem-specific neighborhood structures, and restart mechanism, the proposed algorithm significantly outperforms other comparison algorithms in fixing the studied problem.(c) 2023 Published by Elsevier B.V.

关键词： Mixed-model assembly job-shop scheduling Batch transfer gene expression programming Clustering Simulated annealing

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A GEP model for energy and exergy analysis of LiCl-H2O absorption cooling systems

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第17期43卷 2099-2108页

作者： Sahin, Arzu Sencan Kovaci, Tugba Dikmen, Erkan Isparta Univ Appl Sci Technol Fac TR-32260 Isparta Turkey

Absorption cooling systems use heat energy to generate chilled water rather than electrical energy that is used in vapor compression cycle. Energy and exergy analysis of absorption cooling systems is fairly complex. These analyses required a lot of time and effort. In this study, a new approach using gene expression programming (GEP) to determine coefficient of performance (COP) and exergy efficiency of the LiCl-H2O absorption system was used. First, COP and exergy efficiency values of the LiCl-H2O absorption system values by using EES software were calculated. Obtained values with EES software were used as datasheet for GEP model. New formulations were presented for the determination of the COP and exergy efficiency values of the LiCl-H2O absorption cooling system. It was found that the results of the GEP model are in agreement with actual values obtained with the EES software. GEP model will be useful for determining the COP and exergy efficiency of absorption cooling systems in a shorter time and at appropriate accuracy.

关键词： gene expression programming absorption cooling LiCl-H2O energy exergy

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