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Assessment and prediction of the mechanical properties of ternary geopolymer concrete

Jinliang LIU; Wei ZHAO; Xincheng SU; Xuefeng XIE

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1436-1452 doi: 10.1007/s11709-022-0889-y

Abstract: Appropriate prediction models with the R2 of 0.945 and 0.987 for predicting flexural

Keywords： Ternary Geopolymer Concrete (TGC)     alkaline activator modulus     alkali content     mechanical properties     assessment    

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Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 665-681 doi: 10.1007/s11709-021-0713-0

Abstract: adaptive neuro-fuzzy inference system–biogeographic based optimization (ANFIS-BBO) provides superior prediction

Keywords： long contraction scour     prediction     uncertainty     ANFIS model     meta-heuristic algorithm    

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Meteorological technology application and development in wind energy resources utilization

Song Lili,Zhou Rongwei,Yang Zhenbin,Zhu Rong,

Strategic Study of CAE 2012, Volume 14, Issue 9,   Pages 96-101

Abstract: pointed out the key technical issues and direction should be noted in the applying process of wind energy assessment, numerical simulation and numerical prediction technology.

Keywords： wind power resource     meteorological technology     observation and assessment     simulation and prediction    

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Summary of Research Progress and Methods of Disruptive Technology in China and Abroad

Sun Yongfu, Wang Liheng, Lu Chunhua, Sun Zongtan, Wang Kunsheng, Xu Yuan

Strategic Study of CAE 2018, Volume 20, Issue 6,   Pages 14-23 doi: 10.15302/J-SSCAE-2018.06.003

Abstract:

At present, technological innovation has entered an unprecedentedly intensive and active period worldwide, while some major disruptive technologies are constantly emerging. These disruptive technologies have accelerated the iteration of new industries and formats, are profoundly influencing the balance among national powers, and will contribute in reshaping the world economic structure and the international competition pattern. To seize this opportunity given by the times, countries are now actively engaged in early identification and nurture of disruptive technologies. In this paper, research reports on disruptive technologies in China and abroad are extensively followed, and research progress and methods of disruptive technologies are summarized, analyzed and evaluated. This paper also puts forward some suggestions to promote the scientific development of disruptive technology research in China, including establishment of specialized think tanks by concentrating superior resources, establishment of a scientific technology evaluation system, etc.

Keywords： disruptive technology     assessment and prediction     methods    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: Proper assessment is crucial in the initial design stages.

Keywords： pile responses     excavation     prediction     deflection     bending moments    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4,   Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Structural pavement assessment in Germany

Lutz PINKOFSKY, Dirk JANSEN

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 2,   Pages 183-191 doi: 10.1007/s11709-017-0412-z

Abstract: motorways and trunk roads encourages the use of innovative, sound and reliable methods for the structural assessmentEssential elements for this are data, which allow a reliable assessment.For a holistic approach to structural pavement assessment performance orientated measurements will beThe paper summarizes the actual assessment procedures in Germany as well as the ongoing work on the development

Keywords： pavement assessment     Germany     structure     system    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units     condition-based maintenance     trend prediction    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1,   Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationHerein we used seven descriptors based on understanding dissolution behavior to establish two solubility predictionThe solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Title Author Date Type Operation

Assessment and prediction of the mechanical properties of ternary geopolymer concrete

Jinliang LIU; Wei ZHAO; Xincheng SU; Xuefeng XIE

Journal Article

Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

Journal Article

Meteorological technology application and development in wind energy resources utilization

Song Lili,Zhou Rongwei,Yang Zhenbin,Zhu Rong,

Journal Article

Summary of Research Progress and Methods of Disruptive Technology in China and Abroad

Sun Yongfu, Wang Liheng, Lu Chunhua, Sun Zongtan, Wang Kunsheng, Xu Yuan

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Structural pavement assessment in Germany

Lutz PINKOFSKY, Dirk JANSEN

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article