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Compressive strength of biopolymer-stabilized residual granitic soil using polybutylene succinate and xanthan gum:A mechanical-microstructural study 认领 引用
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作者 Fazal E.Jalal Mudassir Iqbal +2 位作者 Xiaohua Bao Syed Taseer Abbas Jaffar Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期673-701,共29页
The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)bi... The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil. 展开更多
关键词 Residual granitic soil Polybutylene succinate Xanthan gum(XG)Unconfined compression strength(UCS) Random forest(RF) eXtreme gradient boost(XGBoost)method
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Prediction of small-strain elastic stiffness of natural and artificial soft rocks subjected to freeze-thaw cycles 认领 引用
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作者 Muhammad Ali Ayesha Zubair +2 位作者 Zainab Farooq Khalid Farooq Zubair Masoud 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第6期3546-3562,共17页
The deterioration of soft rocks caused by freeze-thaw(F-T)climatic cycles results in huge structural and financial loss for foundation systems placed on soft rocks prone to F-T actions.In this study,cementtreated sand... The deterioration of soft rocks caused by freeze-thaw(F-T)climatic cycles results in huge structural and financial loss for foundation systems placed on soft rocks prone to F-T actions.In this study,cementtreated sand(CTS)and natural soft shale were subjected to unconfined compression and splitting tensile strength tests for evaluation of unconfined compressive strength(UCS,qu),initial small-strain Young’s modulus(Eo)using linear displacement transducers(LDT)up to a small strain of 0.001%,and secant elastic modulus(E50)using linear variable differential transducers(LVDTs)up to a large strain of 6%before and after reproduced laboratory weathering(RLW)cycles(-20℃e-110℃).The results showed that eight F-T cycles caused a reduction in qu,E50 and Eo,which was 8.6,15.1,and 14.5 times for the CTS,and 2.2,3.5,and 5.3 times for the natural shale,respectively.The tensile strength of the CTS and natural rock samples exhibited a degradation of 5.4 times(after the 8th RLW cycle)and 2.7 times(after the 15th RLW cycle),respectively.Novel correlations have been developed to predict Eo(response)from the parameters qu and E50(predictors)using MATLAB software's curve fitter.The findings of this study will assist in the design of foundations in soft rocks subjected to freezing and thawing.The analysis of variance(ANOVA)indicated 95%confidence in data health for the design of retaining walls,building foundations,excavation in soft rock,large-diameter borehole stability,and transportation tunnels in rocks for an operational strain range of 0.1%e0.01%(using LVDT)and a reference strain of less than 0.001%(using LDT). 展开更多
关键词 Artificial soft rock Stiffness Weathering Freeze-thaw(F-T)cycles Small-strain elastic stiffness
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Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-BasedModels 认领 引用 被引量：3
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作者 Feezan Ahmad Xiaowei Tang +2 位作者 Jilei Hu Mahmood Ahmad Behrouz Gordan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期455-487,共33页
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation a... Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research. 展开更多
关键词 Slope stability seismic excitation static condition random tree reduced error pruning tree
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A numerical study of fracture initiation under different loads during hydraulic fracturing 认领 引用 被引量：9
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作者 TANG Shi-bin DONG Zhuo +1 位作者 WANG Jia-xu MAHMOOD Ahmad 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第12期3875-3887,共13页
The fracture initiation behavior for hydraulic fracturing treatments highlighted the necessity of proposing fracture criteria that precisely predict the fracture initiation type and location during the hydraulic fract... The fracture initiation behavior for hydraulic fracturing treatments highlighted the necessity of proposing fracture criteria that precisely predict the fracture initiation type and location during the hydraulic fracturing process.In the present study,a Mohr-Coulomb criterion with a tensile cut-off is incorporated into the finite element code to determine the fracture initiation type and location during the hydraulic fracturing process.This fracture criterion considers the effect of fracture inclination angle,the internal friction angle and the loading conditions on the distribution of stress field around the fracture tip.The results indicate that the internal friction angle resists the shear fracture initiation.Moreover,as the internal friction angle increases,greater external loads are required to maintain the hydraulic fracture extension.Due to the increased pressure of the injected water,the tensile fracture ultimately determines the fracture initiation type.However,the shear fracture preferentially occurs as the stress anisotropy coefficient increases.Both the maximum tensile stress and equivalent maximum shear stress decrease as the stress anisotropy coefficient increases,which indicates that the greater the stress anisotropy coefficient,the higher the external loading required to propagate a new fracture.The numerical results obtained in this paper provide theoretical supports for establishing basis on investigating of the hydraulic fracturing characteristics under different conditions. 展开更多
关键词 hydraulic fracturing internal friction angle stress anisotropy coefficient finite element method
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Numerical Simulation of Liquefaction-Induced Settlement of Existing Structures 认领 引用 被引量：3
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作者 Wuwei Mao Wei Li +3 位作者 Rouzbeh Rasouli Naveed Ahmad Hu Zheng Yu Huang 《Journal of Earth Science》 SCIE CAS CSCD 2023年第2期339-346,共8页
This study presents a numerical approach for assessment of the structure settlement problems in liquefied soils.The fluid dynamics theory was applied to model the liquefied soils and the rigid body dynamics theory was... This study presents a numerical approach for assessment of the structure settlement problems in liquefied soils.The fluid dynamics theory was applied to model the liquefied soils and the rigid body dynamics theory was applied to compute the structure’s translational and angular motions in six degrees of freedom.The dynamic mesh method was applied to modify the deformed mesh due to structure settlement.Shaking table tests were carried out to reproduce the structure’s subsidence behavior and provide validation for the numerical simulations.Results show that the settlement of the structure occurred most rapidly at the very beginning of liquefaction,and then it decreased gradually.Main structure settlements occurred within 10 s after liquefaction.Regarding different structure weights,structure with larger weight yielded larger amount of settlement and higher peak settlement velocity.The results obtained in this study are beneficial for assessment of structure settlement problems in liquefaction prone areas. 展开更多
关键词 liquefaction computational fluid dynamics viscous dynamic mesh shaking table test engineering geology
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Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete 认领 引用 被引量：1
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作者 Mohamed Abdel-Mongy Mudassir Iqbal +3 位作者 M.Farag Ahmed.M.Yosri Fahad Alsharari Saif Eldeen A.S.Yousef 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期525-543,共19页
Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for pre... Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,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 geopolymer.The 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 temperature.The 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 GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The 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. 展开更多
关键词 Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers
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Evaluation of Implementation Preparation for CE based on BEACON Model—Taking Construction Enterprises in Yemen as a Case of Illustration 认领 引用
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作者 Sabrinaji Dahmas Zhongfu Li Mahmood Ahmad 《Frontiers Research of Architecture and Engineering》 2020年第1期7-16,共10页
After decades of civil war,Yemen is in a desperate situation,and the construction industry has been suffering from low productivity and poor performance.In order to improve the productivity for the Yemeni construction... After decades of civil war,Yemen is in a desperate situation,and the construction industry has been suffering from low productivity and poor performance.In order to improve the productivity for the Yemeni construction industry,Construction enterprises must adopt the best and new technologies,new management concepts and philosophies such as Total Quality Management(TQM)and concurrent engineering(CE)owing to achieve improvements in the process of product development.To ensure the successful implementation of CE in the Yemeni construction industry,it is necessary to assess the readiness of those companies to implement CE.In this paper,the BEACON model is used to assess the readiness of the Yemeni companies to implement the concept of CE,that assist in overcoming the construction industry's poor productivity and performance.A study assessing CE implementation readiness will help to promote successful CE implementation in the construction industry and enhance the efficiency of construction companies.The results show that most of the construction companies in the Yemen are not ready to implement CE.The main reason is that the enterprises rely heavily on traditional management methods,and need to improve the organization and management technology.The research results can provide theoretical support for construction companies,especially Yemen companies,to establish basis in implementing an appropriate CE approach for improving performance,and also help international construction companies entering the Yemen construction market to cooperate and implement CE. 展开更多
关键词 Concurrent engineering(CE) Construction industry BEACON model Yemen construction enterprises
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Statistical Modelling of Ultrasonic Pulse Velocity of Fly Ash Based Geopolymer Mortar using Response Surface Methodology 认领 引用
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作者 Muhammad Zahid Nasir Shafiq Muhammad Ali 《Journal of Architectural Environment & Structural Engineering Research》 CAS 2019年第4期11-17,共7页
The fly ash based geopolymer has emerged as a capable and sustainable binder material in construction industry.Ultrasonic pulse velocity(UPV)method is a non-destructive technique for investigating the mechanical perfo... The fly ash based geopolymer has emerged as a capable and sustainable binder material in construction industry.Ultrasonic pulse velocity(UPV)method is a non-destructive technique for investigating the mechanical performance of concrete.Experimental investigation was performed for studying the effect of NaOH Molarity,Na2SiO3/NaOH and curing temperature on the ultrasonic pulse velocity of geopolymer mortar.Experiments were designed based on central composite design(CCD)technique of response surface methodology(RSM).Statistical model was developed and statistically validated and found significant as the difference between adjustable R-squared and predicted R-squared less than 0.2.Finally,the optimized mix proportion was assessed for maximized value of UPV.Experimental validation on the optimized mix reveals the close agreement between experimental and predicted values of UPV with significance level of more than 95%.The proposed technique improves the yield,the reliability of the product and the processes. 展开更多
关键词 Geopolymer Fly ash NaOH molarity Na2SiO3/NaOH Curing temperature Ultrasonic pulse velocity Response surface methodology Optimization
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Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis 认领 引用 被引量：3
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作者 Nima Pirhadi Xusheng Wan +3 位作者 Jianguo Lu Jilei Hu Mahmood Ahmad Farzaneh Tahmoorian 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第4期733-754,共22页
Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate... Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils. 展开更多
关键词 Liquefaction resistance capacity strain energy artificial neural network sensitivity analysis Monte Carlo Simulation
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Evaluating hydrothermal degradation of GFRP composites under sustained loading using explainable machine learning 认领 引用
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作者 Mudassir Iqbal Xiao-Ling Zhao +4 位作者 Hui Li Daxu Zhang Pei-Fu Zhang Xuan Zhao Congshui Yu 《Journal of Ocean Engineering and Science》 SCIE CSCD 2025年第6期982-1001,共20页
The understanding of GFRP composites under hydrothermal conditions and sustained loading offers valuable insights into their performance in challenging environments such as coastal areas,deep-sea structures,and enviro... The understanding of GFRP composites under hydrothermal conditions and sustained loading offers valuable insights into their performance in challenging environments such as coastal areas,deep-sea structures,and environmentally friendly and long-lasting infrastructure solutions.This study examined the mechanical response of GFRP composites subjected to synergic sustained loading and hydrothermal degradation.A prediction application based on the XGBoost machine learning model was developed to estimate the residual mechanical response.The developed model was used to calculate the conversion factor accounting for moisture and temperature-based degradation in FRP composites.The SHAP analysis corroborated the experimental findings such that GFRP-based composites experience an initial rapid decline in mechanical properties when exposed to harsh environments,followed by a slower degradation rate over time.The pultruded vinyl ester-based GFRP composites depict less degradation than polyester-based composites and composites made via vacuum infusion.It was inferred that sustained loading below 30% has no negative impact on the mechanical characteristics of hydrothermal-aged GFRP composites.The degradation became worse for the sustained loading beyond 30% of the ultimate strength of the GFRP composite.Comments are also made on the current recommendations in technical specifications by the European Committee for Standardization CEN/TS 19101 related to moisture and temperature conversion factors.The current work is limited to the mechanical investigation of GFRP composites subjected to hydrothermal degradation.It needs to be extended to other composites such as CFRP and BFRP. 展开更多
关键词 FRP composites Hydrothermal degradation Sustained loading XGBoost Shapley additive explanations CEN/TS 19101
Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model 认领 引用 被引量：2
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作者 Mudassir Iqbal Daxu Zhang Fazal E.Jalal 《Journal of Ocean Engineering and Science》 SCIE 2022年第6期596-606,共11页
GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline conditions.While investigating the durability of GFRP bars in alkaline environment,the effect of surrounding tempe... GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline conditions.While 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 bars.However,the role of variable bar size and volume fraction of fiber have been poorly investigated.Additionally,various structural codes recommend the use of an additional environmental reduction factor to accurately reflect the long-term performance of GFRP bars in harsh environments.This 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 specimens.RF model was optimized,trained,and validated using variety of statistical checks available in the literature.The developed RF model was used for the sensitivity and parametric analysis.Moreover,the formulated RF model was used for studying the long-term performance of GFRP rebars in the alkaline concrete environment.The 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,respectively.The 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 fraction.Also,the long-term performance revealed that the existing recommendations by various codes regarding environmental reduction factors are conservative and therefore needs revision accordingly. 展开更多
关键词 GFRP Seawater and sea sand concrete Durability Mechanical properties Degradation
A novel AI approach for modeling land surface temperature of Freetown,Sierra Leone,based on land-cover changes 认领 引用
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作者 Mosbeh R.Kaloop Mudassir Iqbal +2 位作者 Mohamed T.Elnabwy Elhadi K.Mustafa Jong Wan Hu 《International Journal of Digital Earth》 SCIE EI 2022年第1期1236-1258,共23页
Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST chang... Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes.This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon.Artificial neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction models.Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models.The 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 LST.The results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,respectively.The 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 area.The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST. 展开更多
关键词 Land surface temperature landsat land use/land cover AI modeling
Performance of hot-mix asphalt using polymer-modified bitumen and marble dust as a filler 认领 引用 被引量：4
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作者 Diyar Khan Rawid Khan +2 位作者 Muhammad Tariq Khan Muhammad Alam Tanveer Hassan 《Journal of Traffic and Transportation Engineering(English Edition)》 EI CSCD 2023年第3期385-398,共14页
Marble dust waste powder generated by the marble cutting industry has a high alkalinity.In this research,the use of marble dust(MD)as a mineral filler substitute in hot mixed asphalt(HMA)was evaluated.The Marshall mix... Marble dust waste powder generated by the marble cutting industry has a high alkalinity.In this research,the use of marble dust(MD)as a mineral filler substitute in hot mixed asphalt(HMA)was evaluated.The Marshall mix design was used to determine the optimum bitumen content(OBC)for all of the mixtures.For each of the four MD contents,i.e.,0,2%,4%,and 6%by weight of the total aggregates,four different bitumen percentages were used.The results of the Marshall stability test showed that the optimum filler content was 4%MD.Samples were prepared with 0 MD in the control mix and varying percentages of MD as an alternate filler.In addition,MD aided in increasing the Marshall stability,rutting resistance,and permanent deformation and reduced the fatigue life of asphalt mixtures.As the percentage of MD increases,the rutting resistance and stiffness at high temperatures both increase.As the percentage of MD increases,the fatigue life reduces.Rut resistance in high-temperature conditions can be improved by using MD in HMA as a partial substitute for stone dust(SD).In areas where extensive MD waste is present,MD can be incorporated into HMA mixtures instead of conventional fillers. 展开更多
关键词 Asphalt mixture Marble dust Wheel tracker test Dynamic modulus test Four-point fatigue beam test
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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 认领 引用
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作者 Dung Quang VU Fazal EJALAL +4 位作者 Mudassir IQBAL Dam Duc NGUYEN Duong Kien TRONG Indra PRAKASH Binh Thai PHAM 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2022年第8期1003-1016,共14页
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 hand.... 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 hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance 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 results.However,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 Cs.Thus,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. 展开更多
关键词 shuffled complex evolution artificial bee colony ANFIS concrete compressive strength Vietnam
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Laboratory investigation of flow behavior in an open channel with emerged porous rigid and flexible vegetation 认领 引用
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作者 Kashif Iqbal Usman Ghani +3 位作者 Ghufran Ahmed Pasha Nadir Murtaza Muhammad Kaleem Ullah Naveed Anjum 《Journal of Hydrodynamics》 SCIE EI CSCD 2024年第6期1181-1199,共19页
This study aims to investigate the influence of various vegetation patches with varying porosities on the hydraulic properties of a vegetated open channel under subcritical flow conditions.This research work investiga... This study aims to investigate the influence of various vegetation patches with varying porosities on the hydraulic properties of a vegetated open channel under subcritical flow conditions.This research work investigated three types of vegetation patches:Rigid,flexible,and a combination of the two.In total five vegetation patches with three different porosities for each patch were investigated.Effect of these vegetation patches on various hydraulic parameters such as backwater rise,energy reduction,water surface slope in the vegetation patch,hydraulic jump formation on the downstream side of the vegetation patch,reduction in fluid force index(RFI),moment index(RMI),overflow volume(ΔQ)were studied.The findings revealed that the backwater rise increased in the case of rigid patch as the initial Froude number increased,whereas it decreased in the case of flexible and combined vegetation patches.It was observed that as the porosity increased from low(Pr=0.90)to high(Pr=0.99),the backwater rise decreased for all vegetation patches.The relative energy reduction rate increased for the rigid patch and showed a reverse trend for the flexible and combined vegetation patches with increasing initial Froude number.In the combined vegetation arrangement,the energy reduction values were highest for the alternate rigid and flexible(ARF)vegetation patches and lowest for the longitudinal rigid and flexible(LRF)vegetation patches.This study identified the presence of a hydraulic jump downstream of the vegetation patch,as indicated by the Froude number in the range of 1.0–1.7.The study also found that RFI,RMI,ΔQ had the highest values of 19.05%,19.05%,80.20%.The results of this study provide insight into the impact of vegetation patches with varying porosities on open-channel flow characteristics and can help develop sustainable vegetation management strategies. 展开更多
关键词 Froude number vegetated channel energy reduction hydraulic jump
Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system 认领 引用
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作者 Long Hoang NGUYEN Dung Quang VU +6 位作者 Duc Dam NGUYEN Fazal E.JALAL Mudassir IQBAL Vinh The DANG Hiep Van LE Indra PRAKASH Binh Thai PHAM 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2023年第5期812-826,共15页
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 ... 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 pavements.In 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 pavements.The 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)algorithms.These 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 models.Therefore,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. 展开更多
关键词 falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms
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A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks 认领 引用 被引量：9
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作者 MAHMOOD Ahmad TANG Xiao-wei +2 位作者 QIU Jiang-nan GU Wen-jing FEEZAN Ahmad 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第2期500-516,共17页
Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ... Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon. 展开更多
关键词 Bayesian belief network cone penetration test seismic soil liquefaction interpretive structural modeling structural learning
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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential 认领 引用 被引量：5
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作者 Mahmood AHMAD Xiao-Wei TANG +2 位作者 Jiang-Nan QIU Feezan AHMA Wen-Jing GU 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2021年第2期490-505,共16页
This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the ... This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software Netica.The BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN models.The performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models.The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential.The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models. 展开更多
关键词 seismic soil liquefaction Bayesian belief network cone penetration test parameter learning structural learning
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Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models 认领 引用 被引量：2
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作者 Mudassir Iqbal Khalid Elbaz +2 位作者 Daxu Zhang Lili Hu Fazal E.Jalal 《Journal of Ocean Engineering and Science》 SCIE 2023年第5期546-558,共13页
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... 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 codes.The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars.Therefore,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 model.Various 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 folds.In order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were performed.It was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned GFRP bars.In the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete en-vironment.The sensitivity analysis revealed GFRP bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR. 展开更多
关键词 Gfrp Seawater sea sand concrete Durability Metaheuristic Anfis-pso,anfis-ga ANFIS-SVM
Development of deep neural network model to predict the compressive strength of FRCM confined columns 认领 引用
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作者 Khuong LE-NGUYEN Quyen Cao MINH +1 位作者 Afaq AHMAD Lanh Si HO 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2022年第10期1213-1232,共20页
The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neu... The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar Em.The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy.The ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models.Furthermore,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement prediction.The performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available. 展开更多
关键词 FRCM deep neural networks confinement effect strength model confined concrete
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