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Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Computer Modeling in Engineering & Sciences 2024年第10期141卷 525-543页

作者： Mohamed Abdel-Mongy mudassir iqbal M.Farag Ahmed.M.Yosri Fahad Alsharari Saif Eldeen A.S.Yousef Department of Civil Engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia Department of Civil Engineering University of Engineering and TechnologyPeshawar54890Pakistan Civil Engineering Department Faculty of EngineeringAl-Azhar UniversityCairo11884Egypt Civil Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

关键词： Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers

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Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model

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Journal of Ocean Engineering and Science 2022年第6期7卷 596-606页

作者： mudassir iqbal Daxu Zhang Fazal E.Jalal State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and InfrastructureSchool of Naval ArchitectureOcean&Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and Technology PeshawarPakistan

GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline *** investigating the durability of GFRP bars in alkaline environment,the effect of surrounding temperature and conditioning duration on tensile strength retention(TSR)of GFRP bars is well investigated with laboratory aging of GFRP ***,the role of variable bar size and volume fraction of fiber have been poorly ***,various structural codes recommend the use of an additional environmental reduction factor to accurately reflect the long-term performance of GFRP bars in harsh *** study presents the development of Random Forest(RF)regression model to predict the TSR of laboratory conditioned bars in alkaline environment based on a reliable database comprising 772 tested *** model was optimized,trained,and validated using variety of statistical checks available in the *** developed RF model was used for the sensitivity and parametric ***,the formulated RF model was used for studying the long-term performance of GFRP rebars in the alkaline concrete *** sensitivity analysis exhibited that temperature and pH are among the most influential attributes in TSR,followed by volume fraction of fibers,duration of conditioning,and diameter of the bars,*** bars with larger diameter and high-volume fraction of fibers are less susceptible to degradation in contrast to the small diameter bars and relatively low fiber’s volume ***,the long-term performance revealed that the existing recommendations by various codes regarding environmental reduction factors are conservative and therefore needs revision accordingly.

关键词： GFRP Seawater and sea sand concrete Durability Mechanical properties Degradation

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Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models

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Journal of Ocean Engineering and Science 2023年第5期8卷 546-558页

作者： mudassir iqbal Khalid Elbaz Daxu Zhang Lili Hu Fazal E.Jalal State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and InfrastructureSchool of Naval ArchitectureOcean&Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and Technology PeshawarPakistan MOE Key Laboratory of Intelligent Manufacturing Technology Department of Civil and Environmental EngineeringCollege of EngineeringShantou UniversityShantouGuangdong 515063China Department of Civil Engineering Higher Future Institute of Engineering and TechnologyMansouraEgypt

The long-term durability of glass fiber reinforced polymer(GFRP)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural *** calculation of this factor requires robust models to predict the residual tensile strength of GFRP ***,three robust metaheuristic algorithms,namely particle swarm optimiza-tion(PSO),genetic algorithm(GA),and support vector machine(SVM),were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system(ANFIS)in order to obtain more accurate prediction *** optimized models were developed to predict the tensile strength retention(TSR)of degraded GFRP rebars in typical alkaline environments(e.g.,seawater sea sand concrete(SWSSC)environment in this study).The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging.A to-tal number of 715 experimental laboratory samples were collected in a form of extensive database to be trained.K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal *** order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were *** was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned GFRP *** the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete *** sensitivity analysis revealed GFRP bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR.

关键词： Gfrp Seawater sea sand concrete Durability Metaheuristic Anfis-pso,anfis-ga ANFIS-SVM

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Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

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Frontiers of Structural and Civil Engineering 2022年第8期16卷 1003-1016页

作者： Dung Quang VU Fazal EJALAL mudassir iqbal Dam Duc NGUYEN Duong Kien TRONG Indra PRAKASH Binh Thai PHAM University of Transport Technology Hanoi 100000Vietnam Department of Civil Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other *** were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the *** and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were *** dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid *** of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted ***,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete ***,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

关键词： shuffled complex evolution artificial bee colony ANFIS concrete compressive strength Vietnam

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A novel AI approach for modeling land surface temperature of Freetown,Sierra Leone,based on land-cover changes

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International Journal of Digital Earth 2022年第1期15卷 1236-1258页

作者： Mosbeh R.Kaloop mudassir iqbal Mohamed T.Elnabwy Elhadi K.Mustafa Jong Wan Hu Department of Civil and Environmental Engineering Incheon National UniversityIncheonKorea Incheon Disaster Prevention Research Center Incheon National UniversityIncheonKorea Public Works and Civil Engineering Department Mansoura UniversityMansouraEgypt Department of Civil Engineering University of Engineering and Technology PeshawarPeshwarPakistan National Water Research Center Coastal Research InstituteAlexandriaEgypt Department of Surveying and Geo-Informatics Southwest Jiaotong UniversitySichuanPeople’s Republic of China

Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)*** relationship between LST and LULC indices must be established to accurately estimate the impacts of LST *** study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in *** neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction *** of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed *** extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model *** results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,*** GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic *** sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.

关键词： Land surface temperature landsat land use/land cover AI modeling

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

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Frontiers of Structural and Civil Engineering 2023年第5期17卷 812-826页

作者： Long Hoang NGUYEN Dung Quang VU Duc Dam NGUYEN Fazal E.JALAL mudassir iqbal Vinh The DANG Hiep Van LE Indra PRAKASH Binh Thai PHAM Faculty of Civil Engineering University of Transport TechnologyHanoi 10000Vietnam Department of Civil Engineering State Key Laboratory of Ocean EngineeringShanghai Jiao Tong UniversityShanghai 200025China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of *** this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid *** performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)*** models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR ***,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms

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Smart tailoring of molecular catalysts:Mounting approach to oxygen reduction reaction

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Journal of Materials Science & Technology 2024年第1期168卷 169-184页

作者： Anuj Kumar Mohd Ubaidullah Guoxin Zhang Jasvinder Kaur Saira Ajmal mudassir Hasan Krishna Kumar Yadav Hafiz M.Adeel Sharif Ram K.Gupta Ghulam Yasin Nano-Technology Research Laboratory Department of ChemistryGLA UniversityMathuraUttar Pradesh 281406India Department of Chemistry College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia Department of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdaoShandong 266590China Institute for Advanced Study Shenzhen UniversityShenzhenGuangdong 518060China Department of Chemistry Kansas Polymer Research CenterPittsburg State UniversityPittsburgKS 66762United States Faculty of Science and Technology Madhyanchal Professional UniversityRatibadBhopal 462044India Environmental and Atmospheric Sciences Research Group Scientific Research CenterAl-Ayen UniversityThi-QarNasiriyah 64001Iraq Department of Chemical Engineering King Khalid University Saudi Arabia Department of Chemistry School of SciencesIFTM UniversityMoradabadUttar Pradesh 244102India Research Center for Eco-Environmental Engineering Dongguan University of TechnologyDongguan 523808China School of Environment and Civil Engineering Dongguan University of TechnologyDongguanGuangdong 523808China

Efficient electrocatalytic rupture of energy-rich molecules(H_(2)and O_(2))is a green approach for gener-ating clean energy for modern *** this context,porphyry-type molecular electrocatalysts act intelligently toward oxygen reduction reaction(ORR),a fundamental process in fuel cells,due to their redox-rich chemistry,which involves core metal ions and macrocyclic *** concerned scientific community has tried many times to correlate the ORR intermediates with their formation kinetics and simplify the associated multi H+/e-stages during the ORR process,constructing several volcano plots be-tween catalytic Tafel data,turnover frequencies,and overpotentials for many *** the fact that many review articles on molecular electrocatalysts for ORR have been published,understanding the strategic implications and molecular catalyst intelligence towards homogenous ORR has been poorly *** review examined the relationships between volcano plots of current *** parameters and the Sabatier principle in order to explain the intelligence of molecular electrocatalysts and approaches for their creation,as well as the difficulties and potential prospects of molecular *** facts distinguish this review from previously published articles and will pique the scien-tific community’s interest in avoiding trial-and-error procedures for catalyst creation while also allowing for more exact evaluations of the molecular catalyst’s performance.

关键词： Electrocatalysis Molecular catalysts Oxygen reduction reaction Sabatier principle

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探索基于NiSe2的材料用于下一代能源解决方案：从混合电池超级电容器到高效氧进化催化剂

探索基于NiSe2的材料用于下一代能源解决方案：从混合电池超级电...

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作者： mudassir Hayat 扬州大学

学位级别：硕士

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Impact of Artificial Intelligence on Corporate Leadership

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Journal of Computer and Communications 2024年第4期12卷 40-48页

作者： Daniel Schilling Weiss Nguyen mudassir Mohiddin Shaik School of Business and Technology (SBT) Aspen University Phoenix Arizona USA Information Technology Indiana Wesleyan University Indianapolis Indiana USA

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

关键词： Artificial Intelligence (AI) Corporate Leadership Communication Feedback Systems Tracking Mechanisms Decision-Making Local Machine Learning Models (LLMs) Federated Learning On-Device Learning Differential Privacy Homomorphic Encryption

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Enhancement of the thermo-mechanical properties and ef fi cacy of mixing technique in the preparation of graphene/PVC nanocomposites compared to carbon nanotubes/PVC

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Progress in Natural Science:Materials International 2014年第6期24卷 579-587页

作者： mudassir Hasan Moonyong Lee School of Chemical Engineering Yeungnam University

Thin films of poly vinyl chloride(PVC)/multiwalled carbon nanotubes(MWCNT) and PVC/graphene(GN) nanocomposites were prepared by mixing in the presence of different quantities of nanoparticles. Film casting was performed using tetrahydrofuran as a solvent. The as-prepared PVC/MWCNT and PVC/GN nanocomposites were characterized by scanning electron microscopy, Raman spectroscopy, X-ray diffraction,thermogravimetric analysis, differential scanning calorimetry, dynamic mechanical analysis, and diffused reflectance spectroscopy. Only the PVC/GN nanocomposite films were evaluated further for detailed mechanical analysis because of the poor dispersion of MWCNTs in PVC. The PVC/GN nanocomposite films were thermo-mechanically more stable than the PVC films. These nanocomposites have potential as a replacement material for PVC and PVC/MWCNT owing to their better dispersion and high stability.

关键词： Nanocomposite Mechanical strength Thermal stability Band gap

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