|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Optimization of ultrasound-assisted extraction conditions for euphol from the medicinal plant, Euphorbia tirucalli, using response surface methodology显示文摘 | Quan V Vuong Van Tang Nguyen Dang Trung Thanh | 2015 | Industrial Crops and Products2015，63，1： | 1 |
| 2 | Development of the layer-by-layer biosensor using graphene films: application for cholesterol determination显示文摘 | Hai Binh Nguyen Van Chuc Nguyen Van Tu Nguyen Huu Doan Le Van Quynh Nguyen Thi Thanh Tam Ngo Quan Phuc Do Xuan Nghia Nguyen Ngoc Minh Phan Dai Lam Tran | 2013 | Advances in Natural Sciences: Nanoscience and Nanotechnology2013，，1： | 1 |
| 3 | Biological Community in Submerged Caves and Marine Lakes in ha Long-Cat Ba Area, Vietnam显示文摘 | Nguyen Dang Ngai Dau Van Thao Do Cong Thung Le Thi Thuy Dam Duc Tien Nguyen Van Quan PhamVan Chien | 2015 | Journal of Life Sciences2015，9，11： | 0 |
| 4 | Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms显示文摘Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand.The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams(SB).A database encompassing 1118 experimental findings from the relevant literature was compiled,containing eight distinct factors.Gradient Boosting(GB)technique was developed and evaluated in combination with three different optimization algorithms,namely Particle Swarm Optimization(PSO),Random Annealing Optimization(RA),and Simulated Annealing Optimization(SA).The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables.Shap values and two-dimensional PDP analysis were then carried out.Engineers may use the findings in this work to define beam’s geometrical components and material used to achieve the desired shear strength of SB without reinforcement. | Thuy-Anh NGUYEN Hai-Bang LY Van Quan TRAN | 2022 | Frontiers of Structural and Civil Engineering2022，16，10： | 0 |
| 5 | Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete显示文摘The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design process.This paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from mix design and curing age by a machine learning(ML)technique named the Extreme Gradient Boosting(XGB)algorithm,including non-hybrid and hybrid models.Nine ML techniques,such as Linear regression(LR),K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Decision Trees(DTR),Random Forest(RF),Gradient Boosting(GB),and Artificial Neural Network using two training algorithms LBFGS and SGD(denoted as ANN_LBFGS and ANN_SGD),are also compared with the XGB model.Moreover,the hybrid models of eight ML techniques and Particle Swarm Optimization(PSO)are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model.The highest number of SCC samples available in the literature is collected for building the ML techniques.Compared with previously published works’performance,the proposed XGB method,both hybrid and non-hybrid models,is the most reliable and robust of the examined techniques,and is more accurate than existing ML methods(R2=0.9644,RMSE=4.7801,and MAE=3.4832).Therefore,the XGB model can be used as a practical tool for engineers in predicting the CS of SCC. | Van Quan TRAN Hai-Van Thi MAI Thuy-Anh NGUYEN Hai-Bang LY | 2022 | Frontiers of Structural and Civil Engineering2022，16，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员