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Research on Direction and Location for Danger Prediction of Coal or Rock Dynamic Disaster

Xiao Hohgfei,He Xueqiu,Feng Tao,Wang Enyuan

Strategic Study of CAE 2005, Volume 7, Issue 11,   Pages 81-86

Abstract: order to use the non-touch electromagnetic emission (EME) method to better forecast or predict the dangerThe outburst danger can be monitored by altering the antenna monitoring direction.The available scope of prediction distance can be determined in light of the stress concentration ascertainedspot show that it is feasible to study the direction and location of dangerous areas in predicting the danger

Keywords： rock or coal dynamic disaster     danger prediction     direction and location     deformation and fracture of coal    

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Hongshiyan Landslide Dam Danger Removal and Coordinated Management

Ning Liu

Frontiers of Engineering Management 2014, Volume 1, Issue 3,   Pages 308-317 doi: 10.15302/J-FEM-2014041

Abstract: triggered by an earthquake near Ludian County in Zhaotong City, Yunnan Province, introduces how the danger

Keywords： the Zhaotong Ludian earthquake     danger removal of the landslide dam     coordinated management    

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Hongshiyan landslide dam danger disposal and coordinated management

Liu Ning

Strategic Study of CAE 2014, Volume 16, Issue 10,   Pages 39-46

Abstract: It introduces how the danger disposal plan is drafted and implemented，and analyzes the result of its

Keywords： the Shaotong Ludian earthquake     danger disposal of the landslide dam     coordinated management.    

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Discussion of the Prospective Danger of Earthquake Damage to the Xiamen-Jinmen Area

Peng Funan,Ye Yincan,Pan Guofu,Liu Dujuan

Strategic Study of CAE 2005, Volume 7, Issue 5,   Pages 16-23

Abstract:

It had long been discussed already about the characteristics of the recent earthquake of the Xiamen-Jinmen area. However,attention should be paid that a large shock unexpected before as the 1999 Chi-Chi Large Earthquake in Taiwan west-plain, would take place in this area and its neighborhood and affect the stability of the construction during the long duration after the completion and usage of the future Xiamen-Jinmen Bridge. The probability of such case should be considered from the study of the earthquake mechanism of the surrounding regions,i.e. the Taiwan Strait Basin,Taiwan region (Taiwan Island) and Fujian coastal region respectively. This paper describes and discusses the features of earthquakes occurred and will occur in any one of these 3 regions and how they would affect the Xiamen-Jinmen area wherein the Xiamen-Jinmen Bridge constructed.

Keywords： Xiamen-Jinmen Bridge     Taiwan Strait     danger of earthquake damage    

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Predication of Potential Danger Region (Zone) of Coal and Gas Outburst

Xian Xuefu,Xu Jiang,Wang Hongtu

Strategic Study of CAE 2001, Volume 3, Issue 2,   Pages 39-46

Abstract: Finally, the fundamental method for deviding the potential danger region (zone) of coal and gas outburst

Keywords： dynamical phenomenon in mine     coal and gas outburst     potential danger zone     failure criterion     coefficient    

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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: Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

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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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

Research on Direction and Location for Danger Prediction of Coal or Rock Dynamic Disaster

Xiao Hohgfei,He Xueqiu,Feng Tao,Wang Enyuan

Journal Article

Hongshiyan Landslide Dam Danger Removal and Coordinated Management

Ning Liu

Journal Article

Hongshiyan landslide dam danger disposal and coordinated management

Liu Ning

Journal Article

Discussion of the Prospective Danger of Earthquake Damage to the Xiamen-Jinmen Area

Peng Funan,Ye Yincan,Pan Guofu,Liu Dujuan

Journal Article

Predication of Potential Danger Region (Zone) of Coal and Gas Outburst

Xian Xuefu,Xu Jiang,Wang Hongtu

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

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