An approach for eliminating re-reflected waves

Theoretical and experimental studies were conducted to eliminate the re-reflected waves in a wave channel by installing a wavefilter in front of the wavemaker. A thin porous mesh is installed in front of the wavemaker.to serve as a wavefilter. A porous-effect parameter [Chwang, A. T. and Li, W. (1983), A piston-type porous wavemaker theory. J. Engng Math. 17, 301–313], G₀ = bω/μL₀, is employed to characterize the transmissability of the wavefilter. Theoretical relationships are established between the amplitude of progressive wave and G₀, and the distances between the wavefilter, the wavemaker and the test structure. The proper location for the wavefilter to eliminate re-reflected waves can be determined. Several experimental tests were conducted to verify the theory.Both theoretical and experimental studies show that re-reflected waves can be effectively eliminated by placing a wavefilter at a proper position between the wavemaker and the test structure, provided G₀ ≤ 1. For G₀ > 1 however, the wavefilter would fail.     

Characterization of land subsidence in the Choshui River alluvial fan,Taiwan

Land subsidence in Taiwan has increased rapidly over the past four decades, owing to heavy withdrawal of groundwater. Leveling surveys, layer compressions and groundwater level in individual layers were continuously monitored in the Choshui alluvial fan, western Taiwan. It was found that ground subsidence and layer compression were consistent with the variation of groundwater level. From multi-level compression monitoring, the layers with major compression deformation were identified. Clayey and sandy layers exhibited significant compression deformation. Conceptual models of compression behavior for clays and sands were studied based on the field data. The compression behavior of clays agreed with the Terzaghi consolidation theory. Notable, irrecoverable volumetric strains were also observed in sandy layers. They exhibited obvious elasto-plastic mechanical behavior. The high compressibility of the sand layer in the alluvial fan was attributed to the flaky sand grains in this geological region.     

Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting

Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.     

Deriving landslide dam geometry from remote sensing images for the rapid assessment of critical parameters related to dam-breach hazards

Dam-breaches that cause outburst floods may induce downstream hazards. Because landslide dams can breach soon after they are formed, it is critical to assess the stability quickly to enable prompt action. However, dam geometry, an essential component of hazard evaluation, is not available in most cases. Our research proposes a procedure that utilizes post-landslide orthorectified remote sensing images and the pre-landslide Digital Terrain Model in the Geographic Information System to estimate the geometry of a particular dam. The procedure includes the following three modules: (1) the selection of the reference points on the dam and lake boundaries, (2) the interpolation of the dam-crest elevation, and (3) the estimation of dam-geometry parameters (i.e., the height, length, and width), the catchment area, the volumes of barrier lake and landslides dam. This procedure is demonstrated through a case study of the Namasha Landslide Dam in Taiwan. It was shown the dam-surface elevation estimated from the proposed procedure can approximate the elevation derived from profile leveling after the formation of the landslide dam. Thus, it is feasible to assess the critical parameters required for the landslide dam hazard assessment rapidly once the ortho-photo data are available. The proposed procedure is useful for quick and efficient decision making regarding hazard mitigation.     

Logistic regression model for predicting the failure probability of a landslide dam

Landslides may obstruct river flow and result in landslide dams; they occur in many regions of the world. The formation and disappearance of natural lakes involve a complex earth–surface process. According to the lessons learned from many historical cases, landslide dams usually break down rapidly soon after the formation of the lake. Regarding hazard mitigation, prompt evaluation of the stability of the landslide dam is crucial. Based on a Japanese dataset, this study utilized the logistic regression method and the jack-knife technique to identify the important geomorphic variables, including peak flow (or catchment area), dam height, width and length in sequence, affecting the stability of landslide dams. The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models. Accordingly, the failure probability of a landslide dam can also be evaluated based on this approach. Ten landslide dams (formed after the 1999 Chi-Chi Earthquake, the 2008 Wenchuan Earthquake and 2009 Typhoon Morakot) with complete dam geometry records were adopted as examples of evaluating the failure probability. The stable Tsao-Ling landslide dam, which was induced by the Chi-Chi earthquake, has a failure probability of 27.68% using a model incorporating the catchment area and dam geometry. On the contrary, the Tangjiashan landslide dam, which was artificially breached soon after its formation during the Wenchuan earthquake, has a failure probability as high as 99.54%. Typhoon Morakot induced the Siaolin landslide dam, which was breached within one hour after its formation and has a failure probability of 71.09%. Notably, the failure probability of the earthquake induced cases is reduced if the catchment area in the prediction model is replaced by the peak flow of the dammed stream for these cases. In contrast, the predicted failure probability of the heavy rainfall-induced case increases if the high flow rate of the dammed stream is incorporated into the prediction model. Consequently, it is suggested that the prediction model using the peak flow as causative factor should be used to evaluate the stability of a landslide dam if the peak flow is available. Together with an estimation of the impact of an outburst flood from a landslide-dammed lake, the failure probability of the landslide dam predicted by the proposed logistic regression model could be useful for evaluating the related risk.     

A systematic approach for the assessment of flooding hazard and risk associated with a landslide dam

Inundation caused by landslide dams may occur in the upstream and downstream of the dams. A proper flooding hazard assessment is required for reaction planning and decision-making to mitigate possible flooding hazards caused by landslide dams. Both quick and detailed procedures can be used to evaluate inundation hazards, depending on the available time and information. This paper presents a systematic approach for the assessment of inundation hazards and risks caused by landslide dam formation and breaches. The approach includes the evaluation of dam-breach probability, assessment of upstream inundation hazard, assessment of downstream inundation hazard, and the classification of flooding risk. The proposed assessment of upstream inundation estimates the potential region of inundation and predicts the overtopping time. The risk level of downstream flooding is evaluated using a joint consideration of the breach probability of a landslide dam and the level of flooding hazard, which is classified using a flooding hazard index that indicates the risk of potential inundation. This paper proposes both quick and detailed procedures for the assessments of inundation in both the upstream and downstream of a landslide dam. An example of a landslide dam case study in southern Taiwan was used to demonstrate the applicability of the systematic approach.     

An empirical strength criterion for jet grouted soilcrete

Based on experimental data, this paper presents an empirical strength criterion for jet grouted soilcrete. Cylindrical specimens drilled from construction sites were tested to investigate the strength characteristics of soilcrete. Experiments conducted include the uniaxial compression test, Brazilian test and the triaxial compression test. It was found that soilcrete density increases with increasing depth. The uniaxial compressive strengths obtained were significantly greater than the design values suggested by the JSG Association of Japan. Experimental Poisson's ratios varied from 0.12 to 0.22, which are closer to that for concrete than that for native soil. Based on test results, a parabolic-type empirical criterionτ_ff = N·σ_ff + T_o)^M is proposed to describe the strength behaviour of soilcrete under low and tensile stress regions.     

Discriminant analysis of the geomorphic characteristics and stability of landslide dams

Landslides can cause the formation of dams, but these dams often fail soon after lake formation. Thus, rapidly evaluating the stability of a landslide dam is crucial for effective hazard mitigation. This study utilizes discriminant analysis based on a Japanese dataset consisting of 43 well documented landslide dams to determine the significant variables, including log-transformed peak flow (or catchment area), and log-transformed dam height, width and length in hierarchical order, which affect the stability of a landslide dam. The high overall prediction power (88.4% of the 43 training cases are correctly classified) and the high cross-validation accuracy (86%) demonstrate the robustness of the proposed discriminant models PHWL (with variables including log-transformed peak flow, and log-transformed dam height, width and length) and AHWL (with variables including log-transformed catchment area, and log-transformed dam height, width and length). Compared to a previously proposed “DBI” index-based graphic approach, the discriminant model AHV – which uses the log-transformed catchment area, dam height, and dam volume as relevant variables – shows better ability to evaluate the stability of landslide dams. Although these discriminant models are established using a Japanese dataset only, the present multivariate statistical approach can be applied for an expanded dataset without any difficulty when more completely documented worldwide landslide-dam data are available.