Hieu Chi Phan
Le Quy Don Technical University
Reduction (mathematics)Risk analysis (engineering)LeveeParametric statisticsGeologyCoefficient of determinationFinite element methodMean squared errorPipeline (computing)Redundancy (engineering)Nonlinear systemRandom forestTest setPipeline transportMains electricityAdaptive neuro fuzzy inference systemGeotechnical engineeringReliability engineeringPrioritizationDistribution networksBurst pressureFailure probabilityMathematicsComputer scienceArtificial neural networkStructural engineeringDifferential evolutionAlgebraic connectivity
Publications 8
AbstractThe vulnerability of water distribution networks (WDNs) to water-main breaks is used for prioritizing pipes in the network for maintenance planning. In this paper, a parameter of graph theo...
#1Hieu Chi Phan (Le Quy Don Technical University)H-Index: 5
#2Tien-Thinh LeH-Index: 18
Last. Tiep Duc Pham (Le Quy Don Technical University)H-Index: 1
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Abstract Buried pipes suffer from various natural and human-related phenomena leading to the bending forces on such structures. The analytical models face obstacles such as the complications in modeling material behavior and the local stress concentration due to the appearance of defects are combined. This causes the accumulative over and under-estimation of pipe capacity due to the idealizations of full plastic stress distribution and location of defects at the most dangerous area, respectively...
3 CitationsSource
#1Hieu Chi PhanH-Index: 3
#1Hieu Chi Phan (Le Quy Don Technical University)H-Index: 5
Last. Ashutosh Sutra DharH-Index: 9
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Abstract Establishing an accurate model to predict burst pressure is desired, which has been developed for decades. Although various models have been developed, errors unavoidably appear in the prediction of burst pressures because of the uncertainty in both input variables and nonlinear relationship of such variables to the burst pressure. Consequently, machine learning models, which is a data-driven approach, are potential alternatives. In this paper, various machine learning models such as Ra...
#1Hieu Chi Phan (Le Quy Don Technical University)H-Index: 5
#2Huan Thanh Duong (VNUHCM: Vietnam National University, Ho Chi Minh City)H-Index: 3
Abstract Pipeline is an important and valuable infrastructure for transporting oil and gas which expanding a long distance and working in a corrosive environment. Consequently, corrosion becomes one of the most critical threads for metal material pipeline. The high internal pressure in an oil and gas pipeline is the additional factor leading to the high risk of bursting. Various models predicting the burst pressure of defected pipeline have been developed in literature. However, evaluating burst...
2 CitationsSource
#1Tien-Thinh LeH-Index: 18
#2Hieu Chi Phan (Le Quy Don Technical University)H-Index: 5
The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanic...
4 CitationsSource
#1Huan Thanh Duong (VNUHCM: Vietnam National University, Ho Chi Minh City)H-Index: 3
#2Hieu Chi Phan (Le Quy Don Technical University)H-Index: 5
Last. Nang Duc Bui (Le Quy Don Technical University)H-Index: 2
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Abstract Concrete-filled steel tube (CFT) are widely used as critical members for various types of structures such as bridges, high-rise buildings etc. However, there is a lack of proper models in standards to calculate the capacity of CFT members especially for high strength steel and concrete. This leads to various experiments and simulations conducted and provided in literature and a data-driven is a potential candidate with such plenty of data. The developed model used Artificial Neural Netw...
9 CitationsSource
#1Tiep Duc PhamH-Index: 1
#2Nang Duc BuiH-Index: 2
Last. Hieu Chi PhanH-Index: 5
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