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Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.
Kieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements. ISPRS International Journal of Geo-Information 2021, 10, 42 .
AMA StyleKieu Anh Nguyen, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang. Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements. ISPRS International Journal of Geo-Information. 2021; 10 (1):42.
Chicago/Turabian StyleKieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. 2021. "Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements." ISPRS International Journal of Geo-Information 10, no. 1: 42.
The sediment delivery ratio (SDR) connects the weight of sediments eroded and transported from slopes of a watershed to the weight that eventually enters streams and rivers ending at the watershed outlet. For watershed management agencies, the estimation of annual sediment yield (SY) and the sediment delivery has been a top priority due to the influence that sedimentation has on the holding capacity of reservoirs and the annual economic cost of sediment-related disasters. This study establishes the SEdiment Delivery Distributed (SEDD) model for the Shihmen Reservoir watershed using watershed-wide SDRw and determines the geospatial distribution of individual SDRi and SY in its sub-watersheds. Furthermore, this research considers the statistical and geospatial distribution of SDRi across the two discretizations of sub-watersheds in the study area. It shows the probability density function (PDF) of the SDRi. The watershed-specific coefficient (β) of SDRi is 0.00515 for the Shihmen Reservoir watershed using the recursive method. The SY mean of the entire watershed was determined to be 42.08 t/ha/year. Moreover, maps of the mean SY by 25 and 93 sub-watersheds were proposed for watershed prioritization for future research and remedial works. The outcomes of this study can ameliorate future watershed remediation planning and sediment control by the implementation of geospatial SDRw/SDRi and the inclusion of the sub-watershed prioritization in decision-making. Finally, it is essential to note that the sediment yield modeling can be improved by increased on-site validation and the use of aerial photogrammetry to deliver more updated data to better understand the field situations.
Kent Thomas; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed. Sustainability 2020, 12, 6221 .
AMA StyleKent Thomas, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang. Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed. Sustainability. 2020; 12 (15):6221.
Chicago/Turabian StyleKent Thomas; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. 2020. "Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed." Sustainability 12, no. 15: 6221.
This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.
Kieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan. Sustainability 2020, 12, 2022 .
AMA StyleKieu Anh Nguyen, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang. Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan. Sustainability. 2020; 12 (5):2022.
Chicago/Turabian StyleKieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan." Sustainability 12, no. 5: 2022.
Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.
Kieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Kent Thomas. Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning. Sustainability 2019, 11, 3615 .
AMA StyleKieu Anh Nguyen, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang, Kent Thomas. Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning. Sustainability. 2019; 11 (13):3615.
Chicago/Turabian StyleKieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Kent Thomas. 2019. "Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning." Sustainability 11, no. 13: 3615.
The estimation of soil erosion in Taiwan and many countries of the world is based on the widely used universal soil loss equation (USLE), which includes the factor of soil erodibility (K-factor). In Taiwan, K-factor values are referenced from past research compiled in the Taiwan Soil and Water Conservation Manual, but there is limited data for the downstream area of the Shihmen reservoir watershed. The designated K-factor from the manual cannot be directly applied to large-scale regional levels and also cannot distinguish and clarify the difference of soil erosion between small field plots or subdivisions. In view of the above, this study establishes additional values of K-factor by utilizing the double rings infiltration test and measures of soil physical–chemical properties and increases the spatial resolution of K-factor map for Shihmen reservoir watershed. Furthermore, the established values of K-factors were validated with the designated value set at Fuxing Sanmin from the manual for verifying the correctness of estimates. It is found that the comparative results agree well with established estimates within an allowable error range. Thus, the K-factors established by this study update the previous K-factor system and can be spatially estimated for any area of interest within the Shihmen reservoir watershed and improving upon past limitations.
Bor-Shiun Lin; Chun-Kai Chen; Kent Thomas; Chen-Kun Hsu; Hsing-Chuan Ho. Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed. Sustainability 2019, 11, 355 .
AMA StyleBor-Shiun Lin, Chun-Kai Chen, Kent Thomas, Chen-Kun Hsu, Hsing-Chuan Ho. Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed. Sustainability. 2019; 11 (2):355.
Chicago/Turabian StyleBor-Shiun Lin; Chun-Kai Chen; Kent Thomas; Chen-Kun Hsu; Hsing-Chuan Ho. 2019. "Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed." Sustainability 11, no. 2: 355.
This study compares two sub-watersheds (Chushui and Aiyuzih sub-watersheds) within a single watershed, the Shenmu watershed, which is one of the most active landslide and debris-flow-prone regions of the Taiwan, and investigates the effect of the variation in geological homogeneity and topographical differences on the evolution of landslides. The study analyses a spatial dataset consisting landslide inventory maps of 11 historical events and 17 satellite images spanning 14 years and recommends a methodology of semi-automatic image identification and visual interpretation procedures. The methodology enhances data integrity via the building of landslide inventories based on pre- and post-event maps. A prediction model of the potential landslide areas within the watershed is developed using a combination of environmental factors, causative factors, and the landslide datasets to determine the spatial and probabilistic relationship in landslide activity. The spatial relationships show that landslides are frequent in areas with favorable combinations of causative and environmental factors related to landslide occurrence. Landslides are prevalent on both sides of river courses, and they are the direct suppliers of sediments to the river, leading to sediment-related disasters. Temporally, it is found that typhoon-induced landslides can be subdivided into three distinct time intervals, and the event, which led to the greatest increase in landslide area, can be identified. Furthermore, a verification of the landslide potential map has been conducted for the two temporal periods of pre-1999 Chi-Chi earthquake and from 1999 Chi-Chi earthquake to pre-2009 typhoon Morakot obtaining an overall accuracy of average 80.35%, which agrees well with previous studies. These maps thus obtained can be used as reference information for decision-making to forecast the occurrence of future landslides and for improving early warning systems and rapid response mechanisms.
Bor-Shiun Lin; Kent Thomas; Chun-Kai Chen; Hsing-Chuan Ho. Evaluation of landslides process and potential in Shenmu sub-watersheds, central Taiwan. Landslides 2018, 16, 551 -570.
AMA StyleBor-Shiun Lin, Kent Thomas, Chun-Kai Chen, Hsing-Chuan Ho. Evaluation of landslides process and potential in Shenmu sub-watersheds, central Taiwan. Landslides. 2018; 16 (3):551-570.
Chicago/Turabian StyleBor-Shiun Lin; Kent Thomas; Chun-Kai Chen; Hsing-Chuan Ho. 2018. "Evaluation of landslides process and potential in Shenmu sub-watersheds, central Taiwan." Landslides 16, no. 3: 551-570.
Soil erosion is a global problem that will become worse as a result of climate change. While many parts of the world are speculating about the effect of increased rainfall intensity and frequency on soil erosion, Taiwan’s mountainous areas are already facing the power of rainfall erosivity more than six times the global average. To improve the modeling ability of extreme rainfall conditions on highly rugged terrains, we use two analysis units to simulate soil erosion at the Shihmen reservoir watershed in northern Taiwan. The first one is the grid cell method, which divides the study area into 10 m by 10 m grid cells. The second one is the slope unit method, which divides the study area using natural breaks in landform. We compared the modeling results with field measurements of erosion pins. To our surprise, the grid cell method is much more accurate in predicting soil erosion than the slope unit method, although the slope unit method resembles the real terrains much better than the grid cell method. The average erosion pin measurement is 6.5 mm in the Shihmen reservoir watershed, which is equivalent to 90.6 t ha−1 yr−1 of soil erosion.
Yi-Hsin Liu; Dong-Huang Li; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Fuan Tsai. Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan. Water 2018, 10, 1387 .
AMA StyleYi-Hsin Liu, Dong-Huang Li, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang, Fuan Tsai. Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan. Water. 2018; 10 (10):1387.
Chicago/Turabian StyleYi-Hsin Liu; Dong-Huang Li; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Fuan Tsai. 2018. "Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan." Water 10, no. 10: 1387.
This study compiles the latest regional topographic data from field investigation and remote-sensing images to recalculate parameters of the universal soil loss equation (USLE) model of the Shenmu watershed; also to compensate for reduced accuracy of this model on small-scale slopes, this study incorporates soil erosion pin data which were collected periodically to measure the extent of soil erosion. Firstly, this study utilized the USLE model and soil erosion pin data to compare the soil erosion potential of the Chushui and Aiyuzi subwatersheds and concluded that soil erosion drastically increased if accumulated rainfall exceeded 200 mm; also, erosion depths were greater in the Aiyuzi subwatershed while estimated total erosion volume was higher in the Chushui subwatershed; this was attributed to the larger area of Chushui subwatershed and based on field measurements which supported the results of the USLE model. Secondly, this study utilized modified USLE model to compare the extreme event erosion resulting from typhoon Morakot which revealed that high rainfall intensity and long-duration rainfall events can generate large volume non-point sources of sediment that is estimated to far exceed 7–10 times of the annual soil erosion. Thirdly, this study related the C parameter of the USLE model to the existing land use in the Shenmu watershed using current, real data. Finally, this study established a post-typhoon Morakot soil erosion risk map composed of five categories of risk which was compared with post-event land cover to suggest high-erosion risk zones that may require further monitoring, remediation, and engineering measures to limit soil loss.
B. S. Lin; K. Thomas; C. K. Chen; H. C. Ho. Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters. Paddy and Water Environment 2015, 14, 19 -43.
AMA StyleB. S. Lin, K. Thomas, C. K. Chen, H. C. Ho. Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters. Paddy and Water Environment. 2015; 14 (1):19-43.
Chicago/Turabian StyleB. S. Lin; K. Thomas; C. K. Chen; H. C. Ho. 2015. "Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters." Paddy and Water Environment 14, no. 1: 19-43.
Su-Chin Chen; Hsien-Ter Chou; Shao-Chien Chen; Chun-Hung Wu; Bor-Shiun Lin. Characteristics of rainfall-induced landslides in Miocene formations: A case study of the Shenmu watershed, Central Taiwan. Engineering Geology 2014, 169, 133 -146.
AMA StyleSu-Chin Chen, Hsien-Ter Chou, Shao-Chien Chen, Chun-Hung Wu, Bor-Shiun Lin. Characteristics of rainfall-induced landslides in Miocene formations: A case study of the Shenmu watershed, Central Taiwan. Engineering Geology. 2014; 169 ():133-146.
Chicago/Turabian StyleSu-Chin Chen; Hsien-Ter Chou; Shao-Chien Chen; Chun-Hung Wu; Bor-Shiun Lin. 2014. "Characteristics of rainfall-induced landslides in Miocene formations: A case study of the Shenmu watershed, Central Taiwan." Engineering Geology 169, no. : 133-146.
The occurrence of typhoon Herb in 1996 caused massive landslides in the Shenmu area of Taiwan. Many people died and stream and river beds were covered by meters of debris. Debris flows almost always take place in the Shenmu area during the flood season, especially in the catchment areas around Tsushui river and Aiyuzih river. Anthropogenic and natural factors that cause debris flow occurrences are complex and numerous. The precise conditions of initiation are difficult to be identified, but three factors are generally considered to be the most important ones, i.e. rainfall characteristics, geologic conditions and topography. This study proposes a simple and feasible process that combines remote sensing technology and multi-stage high-precision DTMs from aerial orthoimages and airborne LiDAR with field surveys to establish a connection between three major occurrence factors that trigger debris flows in the Shenmu area.
W.-C. Lo; Bor-Shiun Lin; H.-C. Ho; James L Keck; H.-Y. Yin; H.-Y. Shan. A simple and feasible process for using multi-stage high-precision DTMs, field surveys and rainfall data to study debris flow occurrence factors of Shenmu area, Taiwan. Natural Hazards and Earth System Sciences 2012, 12, 3407 -3419.
AMA StyleW.-C. Lo, Bor-Shiun Lin, H.-C. Ho, James L Keck, H.-Y. Yin, H.-Y. Shan. A simple and feasible process for using multi-stage high-precision DTMs, field surveys and rainfall data to study debris flow occurrence factors of Shenmu area, Taiwan. Natural Hazards and Earth System Sciences. 2012; 12 (11):3407-3419.
Chicago/Turabian StyleW.-C. Lo; Bor-Shiun Lin; H.-C. Ho; James L Keck; H.-Y. Yin; H.-Y. Shan. 2012. "A simple and feasible process for using multi-stage high-precision DTMs, field surveys and rainfall data to study debris flow occurrence factors of Shenmu area, Taiwan." Natural Hazards and Earth System Sciences 12, no. 11: 3407-3419.
This study presents a simplified method to conduct the behavior of pile foundations due to lateral spreading. Wave equation analysis applied to model the pile behavior under seismic motions. According to the concept of force-based method, the authors suggest to adopt the dynamic earth pressure in the wave equation analysis. Numerical procedures were validated with the well-documented cases of damaged piles in 1995 Kobe earthquake. In the meantime, comparing the numerical results with the case histories, one can again evaluate the damaged features of piles and distinguish the actual failure pattern of piles. The predictions and the observations were found in good correspondences.
Bor-Shiun Lin; Der-Wen Chang; Hsing-Chuan Ho. Pile Responses under Seismic Earth Pressures of Lateral Spread Using Wave Equation Analysis. Geoenvironmental Engineering and Geotechnics 2010, 1 .
AMA StyleBor-Shiun Lin, Der-Wen Chang, Hsing-Chuan Ho. Pile Responses under Seismic Earth Pressures of Lateral Spread Using Wave Equation Analysis. Geoenvironmental Engineering and Geotechnics. 2010; ():1.
Chicago/Turabian StyleBor-Shiun Lin; Der-Wen Chang; Hsing-Chuan Ho. 2010. "Pile Responses under Seismic Earth Pressures of Lateral Spread Using Wave Equation Analysis." Geoenvironmental Engineering and Geotechnics , no. : 1.
The aim of this study is to evaluate the probability of seismic hazard for the Taipei metropolitan area in northern Taiwan from readily available information, including the attenuation relationship of peak ground acceleration (PGA), tectonic settings, fault-slip data, and seismicity. The PGA seismic hazard mapping reveals that the hazard level in this area increases going from northwest to southeast and southwest. There are four important earthquake sources that contribute to the hazard level: (1) the plate-boundary interface (subduction zone interface) located offshore of the Ilan plain; (2) the intraslab subduction zone underneath Taipei itself; (3) the crustal areal sources in eastern Taiwan and central Taiwan; and (4) the nearby active Shanchiao fault. The slip-rate of the targeted fault is relatively low, and therefore not the most dangerous earthquake source revealed in the 475-year return period. However, there is no doubt that the target fault is the control source in the 2475-year return period. Furthermore, higher PGAs are predicted using the attenuation relationship of subduction zone earthquake sources rather than crustal earthquake sources, meaning an increase of the seismic hazard level over previous estimates. Consequently, more attention needs to be paid to subduction zone sources when considering mitigation of seismic hazards in northern Taiwan
Chin-Tung Cheng; Chyi-Tyi Lee; Po-Shen Lin; Bor-Shiun Lin; Yi-Ben Tsai; Syi-Jang Chiou. Probabilistic Earthquake Hazard in Metropolitan Taipei and Its Surrounding Regions. Terrestrial, Atmospheric and Oceanic Sciences 2010, 21, 429 .
AMA StyleChin-Tung Cheng, Chyi-Tyi Lee, Po-Shen Lin, Bor-Shiun Lin, Yi-Ben Tsai, Syi-Jang Chiou. Probabilistic Earthquake Hazard in Metropolitan Taipei and Its Surrounding Regions. Terrestrial, Atmospheric and Oceanic Sciences. 2010; 21 (3):429.
Chicago/Turabian StyleChin-Tung Cheng; Chyi-Tyi Lee; Po-Shen Lin; Bor-Shiun Lin; Yi-Ben Tsai; Syi-Jang Chiou. 2010. "Probabilistic Earthquake Hazard in Metropolitan Taipei and Its Surrounding Regions." Terrestrial, Atmospheric and Oceanic Sciences 21, no. 3: 429.
C Hsiao; S Chi; C Cheng; B Lin; B Ku. Prediction on volume of landslide in Shih-Men reservoir watershed in Taiwan from field investigation and historical terrain migration information. Prediction and Simulation Methods for Geohazard Mitigation 2009, 1 .
AMA StyleC Hsiao, S Chi, C Cheng, B Lin, B Ku. Prediction on volume of landslide in Shih-Men reservoir watershed in Taiwan from field investigation and historical terrain migration information. Prediction and Simulation Methods for Geohazard Mitigation. 2009; ():1.
Chicago/Turabian StyleC Hsiao; S Chi; C Cheng; B Lin; B Ku. 2009. "Prediction on volume of landslide in Shih-Men reservoir watershed in Taiwan from field investigation and historical terrain migration information." Prediction and Simulation Methods for Geohazard Mitigation , no. : 1.
B Lin; C Kao; W Leung; C Hsu; C Cheng; J Lian. Prediction on landslides induced by heavy rainfalls and typhoons for Shih-Men watershed in Taiwan using SHALSTAB program. Prediction and Simulation Methods for Geohazard Mitigation 2009, 1 .
AMA StyleB Lin, C Kao, W Leung, C Hsu, C Cheng, J Lian. Prediction on landslides induced by heavy rainfalls and typhoons for Shih-Men watershed in Taiwan using SHALSTAB program. Prediction and Simulation Methods for Geohazard Mitigation. 2009; ():1.
Chicago/Turabian StyleB Lin; C Kao; W Leung; C Hsu; C Cheng; J Lian. 2009. "Prediction on landslides induced by heavy rainfalls and typhoons for Shih-Men watershed in Taiwan using SHALSTAB program." Prediction and Simulation Methods for Geohazard Mitigation , no. : 1.
Finite difference (FD) solutions for static and dynamic Winkler's foundation models are applicable in modeling pile responses. In this paper, relevant solutions on liquefaction induced lateral spreading are presented. Direct and indirect earth-pressure approximations and implementations are introduced. Pile foundation failures of 1995 Kobe earthquake were examined using these models. It was found that both the static and dynamic analyses could provide rational pile displacements in agreement with field observations. The largest pile displacements were found at the pile head from static modeling and the dynamic one using indirect earth pressures. For dynamic solutions with direct earth pressure approximations, maximum pile displacements were found at pile tip. These solutions seem reasonable to model different types of lateral spreading. The mechanism of ground motion, strongly affected by geologic and geographic site conditions as well as the soil-foundation-structure interactions, needs to be carefully verified before applying these solutions.
D. W. Chang; Bor-Shiun Lin; C. H. Yeh; S. H. Cheng. FD Solutions for Static and Dynamic Winkler Models with Lateral Spread Induced Earth Pressures on Piles. Geotechnical Earthquake Engineering and Soil Dynamics IV 2008, 1 .
AMA StyleD. W. Chang, Bor-Shiun Lin, C. H. Yeh, S. H. Cheng. FD Solutions for Static and Dynamic Winkler Models with Lateral Spread Induced Earth Pressures on Piles. Geotechnical Earthquake Engineering and Soil Dynamics IV. 2008; ():1.
Chicago/Turabian StyleD. W. Chang; Bor-Shiun Lin; C. H. Yeh; S. H. Cheng. 2008. "FD Solutions for Static and Dynamic Winkler Models with Lateral Spread Induced Earth Pressures on Piles." Geotechnical Earthquake Engineering and Soil Dynamics IV , no. : 1.
The load distributions of the grouped piles under lateral loads acting from one side of the pile cap could be approximately modeled using the elasticity equations with the assumptions that the underground structure is rigid enough to sustain the loads, and only small deformations of the soils are yielded. Variations of the soil–pile interactions along the depths are therefore negligible for simplicity. This paper presents the analytical modeling using the dynamic pile‐to‐pile interaction factors for 2 × 2 and 2 × 3 grouped piles. The results were found comparative with the experimental and numerical results of other studies. Similar to others' findings, it was shown that the leading pile could carry more static loads than the trailing pile does. For the piles in the perpendicular direction with the static load, the loads would distribute symmetrically with the centerline whereas the middle pile always sustains the smallest load. For steady‐state loads with operating frequencies up to 30 Hz, the pile load distributions would vary significantly with the frequencies. It is interesting to know that designing the pile foundation needs to be cautioned for steady‐state vibrations as they are a problem of machine foundation. However, for transient loads or any harmonic loads acting upon relatively higher frequencies, the pile loads could be regarded as uniformly distributed. It is hoped that the numerical results of this paper will be helpful in the design practice of pile foundation. Copyright © 2008 John Wiley & Sons, Ltd.
Der-Wen Chang; Bor-Shiun Lin; Shih-Hao Cheng. Lateral load distributions on grouped piles from dynamic pile-to-pile interaction factors. International Journal for Numerical and Analytical Methods in Geomechanics 2008, 33, 173 -191.
AMA StyleDer-Wen Chang, Bor-Shiun Lin, Shih-Hao Cheng. Lateral load distributions on grouped piles from dynamic pile-to-pile interaction factors. International Journal for Numerical and Analytical Methods in Geomechanics. 2008; 33 (2):173-191.
Chicago/Turabian StyleDer-Wen Chang; Bor-Shiun Lin; Shih-Hao Cheng. 2008. "Lateral load distributions on grouped piles from dynamic pile-to-pile interaction factors." International Journal for Numerical and Analytical Methods in Geomechanics 33, no. 2: 173-191.