Dede Tarwidi

The aim of this study is to develop an efficient numerical scheme that is capable of simulating landslide-generated waves. The numerical scheme is based on the one-layer non-hydrostatic (NH-1L) model, a phase-solving model that can account for weakly dispersive waves. In this paper, the model is extended to include a time-varying solid bed. This NH-1L scheme is very efficient because, at each time step, only a tridiagonal Poisson pressure matrix needs to be solved. In this study, the capability of the NH-1L scheme to simulate landslide-generated waves is demonstrated by executing two types of landslide motion: constant speed and with acceleration and deceleration. Validation was performed using analytical solutions of the linear weakly dispersive (LWD) model, as well as experimental data. The NH-1L model was capable of describing the generation and propagation of water waves by a submarine landslide from relatively intermediate water to shallow water depths.

Wave prediction in a coastal area, especially with complex geometry, requires a numerical simulation with a high-resolution grid to capture wave propagation accurately. The resolution of the grid from global wave forecasting systems is usually too coarse to capture wave propagation in the coastal area. This problem is usually resolved by performing dynamic downscaling that simulates the global wave condition into a smaller domain with a high-resolution grid, which requires a high computational cost. This paper proposes a deep learning-based downscaling method for predicting a significant wave height in the coastal area from global wave forecasting data. We obtain high-resolution wave data by performing a continuous wave simulation using the SWAN model via nested simulations. The dataset is then used as the training data for the deep learning model. Here, we use the Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) as the deep learning models. We choose two study areas, an open sea with a swell-dominated area and a rather close sea with a wind-wave-dominated area. We validate the results of the downscaling with a wave observation, which shows good results.

Overcrowded of sidewalks can reduce the level of satisfaction of pedestrians. The large crowd on the sidewalks results in slowing down time of the pedestrian to arrive in their destination. This studt, focuses on pedestrian flows modelling using the macroscopic model. Numerical approximation of the macroscopic model is formulated as scalar hyperbolic conservation laws. The Lax-Wendroff scheme is used to discretize the equation of conservation laws. The simulation results show that the numerical approximation in term of density confirms the exact solution. In this simulation, the velocity function is obtained by curve fitting of observation data using linear regression method. The observation data, which are consist of velocity-density relation, are obtained from observation of pedestrian flows. The study case of this research conducts on the sidewalks of Braga Street, Bandung, Indonesia. There are two velocity functions used in the simulation, i.e. c ₁ = 0.58206 + −0.94476ρ and c ₂ = 0.59926 + −0.78052ρ, respectively. In performing the velocity function c ₂, the pedestrian leader position is approximately 1 meter in front of the pedestrian leader using the velocity function c ₁ at final time T = 20 seconds and T = 30 seconds. Overall, the numerical experiment shows that the pedestrian leader using the velocity functions c ₂ is faster than by using c ₁.

Damaged and potholes roads can occur due to rain puddles, too many heavy vehicles, poor asphalt quality, or maybe long road life. Damaged roads can hamper activities and endanger the safety of road users. It is necessary to monitor road quality periodically which is conducted by government, so that roads improvement can be done quick as possible. The aim of this study is to build a system that can classify roads surface quality. Support Vector Machine (SVM) classifications method is used to classify roads based on roadworthiness. In this study, 300 road surface data which contains good/smooth and damage quality of road are used. The simulation results show that SVM model can classify road surface data into two classes with average accuracy of 93%. The results can be a recommendation for government to prioritize which roads need to be improved.

Various efforts have been made to prevent coastal erosion. One of the efforts to prevent coastal erosion is to build breakwaters. This paper presents numerical modeling of fluid flow interaction with various shapes of breakwater. Fluid flow impact on different shapes of breakwater, i.e. trapezoidal prism, cylinder, and sphere has been investigated. The three-dimensional numerical modeling is purposed to decisive which breakwaters shape that can reduce the fluid velocity rapidly, compared to other tested breakwaters shapes. In this study, fluid motion is generated by dam break scheme. The fluid motion is governed by momentum and continuity equation. The equations of fluid motion are resolved by smoothed particle hydrodynamics (SPH) method. DualSPHysics, an open-source code based on SPH method, is applied to simulate fluid motion and the interaction with the blocks of breakwater. According to numerical results, the trapezoidal prism shape of breakwater can scale down the fluid velocity faster than the cylinder and sphere shape of breakwater with maximum velocity is about 2.20 m/s. Further, the cylinder shape yields the highest fluid velocity around the breakwater. The trapezoidal prism shape can be used as an effective breakwater.