29 Sep 2025

Asset Condition and Probability of Failure Assessment–A Vision for Civil Works: A Document to Guide Collaboration and Innovation for the US Army Corps of Engineers Civil Works Asset Management System

Abstract: The US Army Corps of Engineers (USACE) is rapidly improving its asset management system through a variety of research projects and other work efforts that focus on how risk, condition, and probability of failure are conceived, communicated, and used for decision-making across the agency. As these projects move forward, it is critical that USACE defines a long-term vision for condition and probability of failure assessments across the entire asset management system. This Special Report defines that vision with the goal of achieving consensus and buy-in from a variety of participants that will need to buy-in to achieve success. An additional benefit to identifying an end vision for this work is to identify collaborative opportunities and any gaps that must be addressed to achieve it.

29 Sep 2025

State of Practice and Recommendations to Enhance Probability of Failure Estimates for Civil Works Infrastructure Components

Abstract: As the US Army Corps of Engineers (USACE) continues to improve its asset management system, it is imperative that maintenance investments across its wide infrastructure portfolio are maximizing risk reduction. A key component of risk is probability of failure. Presently, USACE estimates probability of failure for asset components in a variety of ways across business lines, activities, and decision spaces. This document explores the variations in the state of practice for probability of failure estimates across USACE and contrasts them with available best practices and methodologies. The review found several key gaps between the state of practice and best practices, including a lack of component failure and life data useful for time-to-failure parameter estimates, a lack of codified definitions of failure, no clear and consistent guidance for probability of failure estimates across business lines or decision spaces, and no methodologies that account for environmental variation at a facility. These gaps are addressed by a research strategy that compares and contrasts several probability of failure calculation methods using presently available data, identifies relevant life data for future collection, and defines a framework for investing in improved probability of failure assessments at facilities.

21 Jul 2025

Publications of the U.S. Army Engineer Research and Development Center; Appendix I: FY24 (October 2023–September 2024)

Abstract: Each year, the US Army Engineer Research and Development Center (ERDC) publishes more than 200 reports through the Information Technology Laboratory’s Information Science and Knowledge Management (ISKM) Branch, the publishing authority for ERDC. Annually since 2017, ISKM has compiled a list of the last fiscal year’s publications. This Appendix I to the original collection includes ERDC publications issued October 2023 through September 2024. The publications are grouped according to the technical laboratories or technical program for which they were prepared, and the preface includes procedures for obtaining ERDC reports. Through this compilation, online distribution, and physical collections, ISKM continues to support ERDC, the Army, and the nation.

24 Jun 2025

Analytical Solutions for Coupled Hydromechanical Modeling of Lateral Earth Pressures in Unsaturated Soils

Abstract: Lateral earth pressures in unsaturated soils undergo variations with changes in suction due to infiltration. The infiltration-induced alterations in the pressure head present a coupled hydromechanical problem, where interactions between solids and fluids influence the outcomes. However, existing analytical solutions for determining lateral earth pressures in unsaturated soils do not consider the effects of hydromechanical modeling. This paper presents analytical solutions for coupled hydromechanical modeling of lateral earth pressures in unsaturated soils. For this purpose, an analytical solution for coupled hydromechanical modeling of one-dimensional (vertical) infiltration is integrated into effective stress-based formulations for at-rest, active, and passive earth pressures of unsaturated soils. The solutions are presented for two cases: with and without a consequential drop in groundwater levels during infiltration. The results are verified by comparing them against those obtained from the finite difference method. The findings demonstrate significant differences between coupled and uncoupled results for pressure head and lateral earth pressures for fine-grained soils (characterized by small Gardner’s coefficients) and during transient (short time) conditions. The comparison of analytical and numerical results was very close for both cases and thus illustrates that the Laplace Transform is an accurate and robust method for determining analytical solutions for this problem.

9 Jun 2025

KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements

Abstract: We introduce KANICE, a novel neural architecture that com-bines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KAN-ICE’s 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/m-ferdaus/kanice).

14 May 2025

Robot Operating System Innovations in Autonomous Navigation

Abstract: This report presents the results of simulations conducted in preparation for the 2024 Maneuver Support and Protection Integration Experiments (MSPIX) demonstration. The study aimed to develop and test a system for autonomous navigation in complex environments using advanced algorithms to enable the robot to avoid obstacles and navigate safely and efficiently. The report describes the methodology used to develop and test the autonomous navigation system, including the use of simulation, to evaluate its performance. The results of the simulation tests are presented to highlight the effectiveness of the navigation solution.

9 May 2025

Smart Installation Weather Warning Decision Support

Abstract: Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events.

9 May 2025

An All-Hazards Return on Investment (ROI) Model to Evaluate U.S. Army Installation Resilient Strategies

Abstract: The paper describes our project to develop, verify, and deploy an All-Hazards Return of Investment model for the U.S. Army Engineer Research and Development Center to provide army installations with a decision support tool for evaluating strategies to make existing installation facilities more resilient. The need for increased resilience to extreme weather was required by U.S. code and DoD guidance, as well as an army strategic plan stipulating an ROI model to evaluate relevant resilient strategies. The ERDC integrated the University of Arkansas designed model into a new army installation planning tool and expanded the scope to evaluate resilient options from climate to all hazards. Our methodology included research on policy, data sources, resilient options, and analytical techniques, along with stakeholder interviews and weekly meetings with installation planning tool developers. The ROI model uses standard risk analysis and engineering economics terms and analyzes potential installation hazards and resilient strategies using data in the installation planning tool. The model calculates the expected net present cost without the resilient strategy, with the resilient strategy, and ROI for each. The minimum viable product ROI model was formulated mathematically, coded in Python, verified using hazard scenarios, and provided to the ERDC for implementation.

25 Apr 2025

Analysis Tools and Techniques for Evaluating Quality in Synthetic Data Generated by the Virtual Autonomous Navigation Environment

Abstract: The capability to produce high-quality labeled synthetic image data is an important tool for building and maintaining machine learning datasets. However, ensuring computer-generated data is of high quality is very challenging. This report describes an effort to evaluate and improve synthetic image data generated by the Virtual Autonomous Navigation Environment’s Environment and Sensor Engine (VANE::ESE), as well as documenting a set of tools developed to process, analyze, and train models from, image datasets generated by VANE::ESE. Additionally, the results of several experiments are presented, including an investigation into using explainable AI techniques, and direct comparisons of various models trained on multiple synthetic datasets.

17 Apr 2025

Assessment of Neural Network Augmented Reynolds Averaged Navier Stokes Turbulence Model in Extrapolation Modes

Abstract: A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the machine-learned model from over-training on more prevalent flow regimes, and results in a model with similar accuracy. Turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode. A priori tests reveal model predictability improves as the hill dataset is partially added during training in a partial extrapolation model. These also provide better turbulent kinetic energy and shear stress predictions than RANS in a posteriori tests. Before a machine-learned model is applied for a posteriori tests, a priori tests should be performed.