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Recent developments in marine shipping will have a profound effect on military port construction. The use of large container vessels and super tankers already requires that commercial marine terminals be extensively modified in order to operate efficiently. The Army must develop the capability to construct facilities that are compatible with these new developments. The report furnishes basic reference information for use in selecting sites for military ports and port facilities, fulfilling the requirements of container facilities, and solving engineering problems that new developments in shipping will place on the military engineer. It involves only present state-of-the-art techniques and equipment that permit the off-loading of all types of commercial and military cargo vessels at the earliest practical time after a site has been tactically secured.

Gravity waves generated by underwater explosions and the wave run-up are predicted for a series of tests to be performed in Mono Lake (California). The time histories of the wave profiles are given at various locations in the lake in deep and shallow water where wave recorders will be installed. The times of arrival and heights of wave run-up are determined at three locations on the shoreline of Mono Lake. This prediction is done by making use of the most advanced theories and all available information in that field., See also Volume 2, AD477286 and Volume 3, AD477287.

Reported is an experimental investigation to assess the limits of applicability of linear wave refraction theory in a convergence zone. One bottom topography with parallel circular contours symmetric about the center of the tank was used and three wave periods (1, 2, and 3 sec) were tested along with three wave heights for each period. All tests were conducted for nonbreaking waves. Data were obtained at 100 locations within the model basin for each condition tested. The data were analyzed on the basic assumption that the total energy was equally partitioned between potential energy and kinetic energy. The basic computation performed was to calculate the potential energy transmitted per unit width over one wave period. Measurements of the reflected energy from the underwater topographic variations indicated that such reflections were negligible. This was verified by computations using the long wave theory as well as a theory not requiring the long wave assumption., Doctoral thesis.

Experimental wave data obtained from small scale underwater explosions was analyzed. Consideration was made of all known and available data including those of Project SEAL and others. It was concluded that the only available small-scale field data of sufficient reliability for use in a detailed analysis are those obtained by Waterways Experiment Station scientists in their past and current test series. Accordingly, certain sets of WES data have been analyzed in detail with the major objectives being the investigation of scaling laws and empirical wave height prediction formulae. (Author)

This report presents the linear theory (in its most general form) of water waves produced by explosions and specifies the assumptions which prevail in performing the Mono Lake deep water predictions. (Author), See also Volume 1, AD-477 285 and Volume 3, AD-477 287.

The following study presents an explicit numerical procedure for finding the position of the free surface and the velocities at the free surface due to a deep explosion. The computational time involved with this procedure will be considerably less than that involved if one were to use the explicit analytical solution given in Progress Report I of this series. The procedure suggested is analogous to that suggested in Progress Report II, and was made possible only after the series representing the velocity potential was shown to be absolutely convergent. (Author)

The objective of this report is to present a method of numerically integrating the Kranzer-Keller equations for explosively generated water waves, and to compare results of the integration method with those obtained from the classical method of stationary phase. Particular attention is paid the near-source area. A previous report discusses a method for evaluating the solution for n(r,t) when the accuracy of the stationary phase approximation becomes questionable. The following report reviews this method, and presents the results of some sample calculations. The accuracy of the integration method is analyzed and results of a parameter study on the error bound are presented. A table of the first 200 zeros of Jo(x), the first 100 zeros of J1(x), and the first 100 zeros of J2(x) calculated to 19 decimal places is given. (Author)

69A3551747104, Short-term traffic flow/speed prediction is a significant and challenging research topic as it is closely related to the application of intelligent transportation systems. Due to the variable and random characteristics of the transportation system, raw traffic flow/speed data often contains noise, and predicting it directly may reduce the accuracy and effectiveness of the prediction models. Therefore, a novel hybrid method is established in this project which combines denoising schemes and deep learning model to improve prediction accuracy. The time series denoising schemes include two parts: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and wavelet packet decomposition (WPD). Firstly, the raw traffic flow data are decomposed by CEEMDAN to gain intrinsic mode functions (IMFs) and a residual. Then the WPD algorithm conducts a secondary decomposition to part of the IMFs that still contains complex and high-frequency component signals of the volume series. Traditional deep learning models for time series prediction are usually based on recurrent neural networks, which have the disadvantages of being time-consuming and prone to problems such as gradient explosions or gradient vanishing. This project uses a relatively new approach, called deep echo state network (DeepESN), which can obtain good prediction results while circumventing the above problems. In the experiment, to investigate the prediction performance of the proposed CEEMDAN-WPD-DeepESN model, the LSTM, CEEMDAN-LSTM, CEEMDANWPD-LSTM, DeepESN, CEEMDAN-DeepESN, CEEMDAN-WPD-DeepESN and CEEMDAN-WPD6-DeepESN models are considered to be comparison models. In addition, network-wide traffic state forecasting is a more challenging task, due to the complex spatial dependencies and the time-varying traffic patterns on road networks. To address this challenge, we treat the traffic network as a graph. Combining graph convolution techniques with deep learning methods to capture the spatia

69A3551747104, The primary aim of this project is to enhance our system from the previous STRIDE F4 project to a parallel computing version. The original F4 system, designated as Automatic Safety Diagnosis in Connected Vehicle (CV) Environment, established a computational pipeline for diagnosing near-crash events exclusively using Basic Safety Messages (BSMs). It was implemented using a sequential computing paradigm. The O6 project was conceived to expedite the system by transitioning it to a parallel version. The F4 system comprised a driving anomaly detection model (DAD), a conflict identification model (CIM), and the data-path connecting them. The DAD was primarily situated in the core cloud, while the CIM was positioned within the CVs. Throughout the O6 process, notable advancements in in-vehicle computers (IVCs) were uncovered. In order to align our system with real-world operations, we opted to fully migrate the DAD component to the IVCs. Recognizing Domain-Specific Design (DSD) as the future of parallel computing, we propose configuring DSD for IVCs based on three levels of abstractions: selecting the appropriate chip architecture, programming language, and parallelism module. For the CIM of our system, we recommend utilizing ARM architecture, the C programming language, and leveraging the built-in parallelism of the ARM chip. As for the DAD, we advocate for a complete migration to IVC, utilizing ARM architecture, the Python language on the CPU, and employing multiprocessing for parallel computing.

69A3551747104, Previous researchers found that the most important accident causation factor was the driver’s abnormal driving status, which was associated with driving volatility. And the driving volatility can be traced from the trajectories of the vehicles that were embedded in the BSMs. Based on these findings, we developed an automatic safety diagnosis system for the connected vehicle environment (ASDSCE), a real-time near crash warning tool with a multi-dimensional cloud-based driving anomaly detection (DAD) model and a conflict identification model (CIM) on the individual level specifically configured for BSMs. The architecture of the proposed system is composed of two components: one is in the cloud who collects and stores BSMs of the CVs and determines in batch mode the thresholds of each vehicle; the other is in the in-vehicle subsystem which determines the driving anomalies and detect conflicts. A near crash will be warranted when the traffic situation satisfies both of the following two conditions: (a) a conflict is identified and, (b) at least one of the drivers that is involved in the conflict is in abnormal driving status. The ASDSCE contains the following features: focusing on detecting abnormal drivers instead of normal drivers; using the trajectory data embedded in the BSM to study driving volatility; implementing on the individual drivers instead of the aggregate level; and reducing the model training time in order to leave sufficient time to the involved drivers to perform successful evasive actions. The presented computational pipeline of ASDSCE includes raw data collection, data preprocessing, data analysis, data communication and warning message generation. ASDSCE is built with Python on Visual Studio 2019 using the BSMs from the CV pilot studies and evaluated using the SHRP2 naturalistic driving study crash data.

69A3551747130, Mississippi’s expansive Yazoo clay experiences slope failures and shallow landslides that result in distressed foundations, roadways, and levees that are integral components of the maritime and multimodal transportation infrastructure. The wet-dry cycles inherent in the Mississippi climate cause shrinking and swelling that result in desiccation cracks, and the increased annual precipitation caused by climate change is allowing more moisture to infiltrate the weathered Yazoo clay, which means that the frequency of shallow landslides is expected to increase. The significant cost of remediation is borne by the maintenance budget of the Mississippi Department of Transportation, the US Army Corps of Engineers, and private and public property owners, and finding sustainable solutions that reduce costs and improve mitigation is vital.