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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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Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference Systemin Estimation of Compression Coefficient of Plastic Clay Soil

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha NguyenHai Nadhir Al-Ansari MahdisAmiri Hai-Bang Ly indra prakash Binh Thai Pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam

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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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Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2219-2241页

作者： Huong Thi Thanh Ngo Nguyen Duc Dam Quynh-Anh Thi Bui Nadhir Al-Ansari Romulus Costache Hang Ha Quynh Duy Bui Sy Hung Mai indra prakash Binh Thai Pham University of Transport Technology Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Departement of Geodesy and Geomatics National University of Civil EngineeringHanoi100000Vietnam Faculty of Hydraulic Engineering National University of Civil EngineeringHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of *** types of floods are mostly associated with landslides and erosion of roads within a short *** of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic *** this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,*** the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental *** performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

关键词： Flash flood deep learning neural network(DL) machine learning(ML) receiver operating characteristic curve(ROC) Vietnam

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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1431-1449页

作者： Nguyen Thanh Hoan Nguyen Van Dung Ho Le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran Van Phong Phan Trong Trinh Dam Duc Nguyen Hiep Van Le Hanh Bich Thi Nguyen Mahdis Amiri indra prakash Binh Thai Pham Institute of Geography Vietnam Academy of Science and TechnologyHanoi10000Viet Nam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)Dong DaHanoi10000Viet Nam University of Transport Technology Thanh XuanHa Noi10000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran DDG(R)Geological Survey of India Gandhinagar382010India

Water level predictions in the river,lake and delta play an important role in flood *** year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high *** subsidence may also aggravate flooding problems in this ***,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and *** are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate *** this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study *** of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML *** data of 19 years period was divided into 70:30 ratio for the *** data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the *** of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study ***,the Bagging-based hybrid models are slightly better than another model such as ***,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

关键词： Computational techniques bagging water level time series algorithms

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A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling:A case study in the Himalayan area, India

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International Journal of Sediment Research 2018年第2期33卷 157-170页

作者： Binh Thai Pham Ataollah Shirzadi Dieu Tien Bui indra prakash M.B.Dholakia Department of Civil Engineering Gujarat Technological University Geographic Information System Group Department of Business and IT University College of Southeast Norway 5. Department of Science ε Technology Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG) 6. Department of Civil Engineering LDCE Gujarat Technological University Department of Geotechnical Engineering University of Transport Technology Department of Rangeland and Watershed Management College of Natural Resources University of Kurdistan

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function(RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area(India). The proposed approach is an integration of the Radial Basis Function(RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters(slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall,distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic(ROC) curve, area under the ROC curve(AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression(LR), Multi-layer Perceptron Neural Networks(MLP Neural Nets), Na(?)ve Bayes(NB), and the hybrid model of Rotation Forest and Decision Trees(RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models(LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.

关键词： Landslide GIS Rotation Forest Radial Base Function Neural Network India

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Novel CMS lines in pigeonpea[Cajanus cajan(L.)Millspaugh]derived from cytoplasmic substitutions,and their effective restoration and deployment in hybrid breeding

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The Crop Journal 2017年第1期5卷 89-94页

作者： Abhishek Bohra Rintu Jha indra prakash Singh Gaurav Pandey Shalini Pareek Partha Sarathi Basu Sushil Kumar Chaturvedi Narendra Pratap Singh ICAR Indian Institute of Pulses Research(IIPR)Kanpur 208024India

The availability of stable cytoplasmic male sterile(CMS or A) lines coupled with a robust restoration system(R lines) is an essential prerequisite for efficient hybrid ***-enabled hybrid technology holds immense potential to enhance the long-stagnant productivity of pigeonpea. In the present investigation, cytoplasmic substitutions were made in the nuclear backgrounds of early-maturing pigeonpea varieties or lines. Three new CMS lines(ICPL 88039 A, Pusa 992 A, and DPP 3-2A) resulted from genetic crosses involving cytoplasmic donors from A2(GT 288A) and A4(ICPA 2089) categories. In addition to visual inspection of anthers, pollen-staining techniques and scanning electron microscopy(SEM)analysis were used to confirm pollen sterility. Further, given the relevance of the plant mitochondrial genome to CMS manifestation, 25 mitochondrion-specific DNA markers were assayed on these newly developed A lines and isogenic maintainer(B) lines. DNA polymorphism between Pusa 992 A and Pusa 992 B as revealed by the nad7a_del marker confirmed the successful combination of sterilizing cytoplasm(A4) and nonrestoring nuclear background(Pusa 992). Such cytoplasm-specific DNA markers are required for A2-CMS as well. Further, to assess restoration ability, potential restorers were crossed with these CMS lines, and as a consequence, promising A × R combinations exhibiting 100%pollen fertility could be identified. In parallel, we also analyzed the inheritance patterns underlying fertility restoration using ICPL 88039A-derived F2 and BC1F1populations,and established a monogenic dominant model to explain the phenomenon of A2-CMS restoration. In summary, we report the successful development of new CMS lines and describe their effective deployment in hybrid breeding of pigeonpea.

关键词： Cytoplasm Hybrid Mitochondria Pigeonpea Restorer Sterility

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IR Spectral Analysis of Diterpene Glycosides Isolated from <i>Stevia rebaudiana</i>

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Food and Nutrition Sciences 2012年第10期3卷 1467-1471页

作者： Venkata Sai prakash Chaturvedula Christopher Mubarak indra prakash Analytical Chemistry Department The Coca-Cola Company Global Research and Development One Coca-Cola Plaza Atlanta GA 30313 USA Organic Chemistry Department The Coca-Cola Company Global Research and Development One Coca-Cola Plaza Atlanta GA 30313 USA

The objective is to identify the Infra-Red (IR) spectral analysis of the diterpene glycosides present in the commercial extracts of Stevia rebaudiana was achieved by PerkinElmer Spectrum 400 Fourier Transform (FT) spectrometer employing a PerkinElmer Universal Attenuated Total Reflection (ATR) accessory. Using this technique the IR spectral pattern of 15 steviol glycosides which belongs to three different classes of ent-kaurane diterpene glycosides namely ent-13-hydroxykaur-16-en-19-oic acid, ent-13-hydroxykaur-15-en-19-oic acid, and 13-methyl-16-oxo-17-norent- kauran-19-oic acid were identified. From the wave numbers found for all 15 steviol glycosides, it was observed that that though there are differences in the number of sugar units, nature of sugar units, and their attachments;there are not any notable differences in the IR values.

关键词： Stevia rebaudiana Diterpene Glycosides IR Spectral Data

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A Study on Comparative Fertility Restoration in A2 and A4 Cytoplasms and Its Implication in Breeding Hybrid Pigeonpea [Cajanus cajan (L.) Millspaugh]

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American Journal of Plant Sciences 2015年第2期6卷 385-391页

作者： Arbind K. Choudhary indra prakash Singh Indian Institute of Pulses Research (IIPR) Kanpur India

Exploitation of hybrid vigour has been visualized as the most efficient option for increasing productivity in pigeonpea [Cajanus cajan (L.) Millspaugh]. Cytoplasms from various wild relatives of pigeonpea have been transferred to develop CMS lines in the background of cultivated pigeonpea. However, A2 (Cajanus scarabaeoides) and A4 (Cajanus cajanifolius) cytoplasms have been utilized most frequently. In order to study fertility restoration efficiency in F1 hybrids having either A2 or A4 cytoplasms, an experiment was conducted at the Indian Institute of Pulses Research (IIPR), Kanpur during 2008-2012. Four CMS lines namely Hy4A, H28A (each with A2 cytoplasm), ICP 2039A and ICP 2043A (both with A4 cytoplasm) were crossed with ten genotypes/restorers of long duration pigeonpea for two years. The F1 hybrids so-obtained were assessed in the succeeding years for pollen fertility and pod setting. All the pollinators except IPA 203 restored fertility in F1 hybrids derived from ICP 2039A and ICP 2043A (both having A4 cytoplasm). However, none of the restorers were effective in restoring fertility in hybrids derived from Hy4A and H28A (each with A2 cytoplasm). This could be ascribed to undesirable linkage drag still present in these two CMS lines having A2 cytoplasm. The F2 progenies derived from 4 hybrids (ICP 2039A × NA-1, ICP 2039A × Bahar, ICP 2043A × NA-1 and ICP 2043A × Bahar) segregated approximately into 3 fertile: 1 sterile plants. However, 2 F2 progenies having Pusa 9 as the restorer revealed approximately 15 fertile:1 sterile ratio. Thus monogenic and digenic duplicate gene action with complete dominance for fertility restoration was observed in F1 hybrids derived from CMS lines having A4 cytoplasm. F3 progenies from individual F2 plants of these crosses also confirmed the same pattern of fertility restoration. This study indicated that CMS lines based on A4 cytoplasm would be more desirable as these might have more number of restorers compared to those having A2 c

关键词： Cajanus cajan CMS Lines A4 Cytoplasm Fertility Restoration Hybrid Pigeonpea

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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan Hai Yen Binh Thai Pham Tran Van Phong Duong Hai Ha Romulus Costache Hiep Van Le Huu Duy Nguyen Mahdis Amiri Nguyen Van Tao indra prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning Hybrid models Groundwater potential GIS Vietnam

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