Results for “factorization machine” — Page 13

DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗

NASA NTRS1977

Evaluation of a low aspect ratio small axial compressor stage, volume 1

A program was conducted to evaluate the effects of scaling, tip clearance, and IGV reset on the performance of a low aspect ratio compressor stage. Stage design was obtained by scaling an existing single stage compressor by a linear factor of 0.304. The design objective was to maintain the meanline velocity field of the base machine in the smaller size. Adjustments were made to account for predicted blockage differences and to chord lengths and airfoil edge radii to obtain reasonable blade geometries. Meanline velocity diagrams of the base stage were not maintained at the scaled size. At design speed and flowrate the scaled stage achieved a pressure ratio of 1.423, adiabatic efficiency of 0.822, and surge margin of 18.5%. The corresponding performance parameters for the base stage were 1.480, 0.872, and 25.2%, respectively. The base stage demonstrated a peak efficiency at design speed of 0.872; the scaled stage achieved a level of 0.838. When the scaled stage rotor and stator tip clearances were doubled, the stage achieved a pressure ratio of 1.413, efficiency of 0.799, and surge margin of 16.0% at the design flowrate. The peak stage efficiency at design speed was 0.825 with the increased clearance. Increased prewhirl lowered the stage pressure ratio as expected. Stage efficiency was maintained with ten degrees of increased prewhirl and then decreased substantially with ten additional degrees of reset.

Sawyer, C. W., III↗

NASA NTRS1977

Experience with a vectorized general circulation weather model on Star-100

A version of an atmospheric general circulation model was vectorized to run on a CDC STAR 100. The numerical model was coded and run in two different vector languages, CDC and LRLTRAN. A factor of 10 speed improvement over an IBM 360/95 was realized. Efficient use of the STAR machine required some redesigning of algorithms and logic. This precludes the application of vectorizing compilers on the original scalar code to achieve the same results. Vector languages permit a more natural and efficient formulation for such numerical codes.

Soll, D. B.↗

NASA NTRS1984

The Strength of the Metal. Aluminum Oxide Interface

The strength of the interface between metals and aluminum oxide is an important factor in the successful operation of devices found throughout modern technology. One finds the interface in machine tools, jet engines, and microelectronic integrated circuits. The strength of the interface, however, should be strong or weak depending on the application. The diverse technological demands have led to some general ideas concerning the origin of the interfacial strength, and have stimulated fundamental research on the problem. Present status of our understanding of the source of the strength of the metal - aluminum oxide interface in terms of interatomic bonds are reviewed. Some future directions for research are suggested.

Pepper, S. V.↗

NASA NTRS1988

Management of human error by design

Design-induced errors and error prevention as well as the concept of lines of defense against human error are discussed. The concept of human error prevention, whose main focus has been on hardware, is extended to other features of the human-machine interface vulnerable to design-induced errors. In particular, it is pointed out that human factors and human error prevention should be part of the process of transport certification. Also, the concept of error tolerant systems is considered as a last line of defense against error.

Wiener, Earl↗

NASA NTRS2001

Astrobiology Sample Analysis as a Design Driver

This effort supports the Astrobiology Objective 8 the Search for LIFE ON MARS PAST AND PRESENT -(Astrobiology Program Office, 1998, p.7). The essential trade analysis is between returning very small samples to the Earth while protecting them versus in situ analysis on Mars. Developing these explicit parameters encompasses design, instrumentation, system integration, human factors and surface operations for both alternatives. This allocation of capability approach incorporates a "humans and machines in the loop" model that recognizes that every exploration system involves both humans and automated systems. The question is where in the loop they occur whether on Earth, in the Mars Base, in the rover or creeping over the Mars surface.

Cohen, Marc M.↗

NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗

DOE OSTI2025

Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1978

Man-machine interface analysis of the flight design system

The objective of the current effort was to perform a broad analysis of the human factors issues involved in the design of the Flight Design System (FDS). The analysis was intended to include characteristics of the system itself, such as: (1) basic structure and functional capabilities of FDS; (2) user backgrounds, capabilities, and possible modes of use; (3) FDS interactive dialogue, problem solving aids; (4) system data management capabilities; and to include, as well, such system related matters as: (1) flight design team structure; (2) roles of technicians; (3) user training; and (4) methods of evaluating system performance. Wherever possible, specific recommendations are made. In other cases, the issues which seem most important are identified. In some cases, additional analyses or experiments which might provide resolution are suggested.

Ramsey, H. R.↗

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRS2003

Gravitational Effects on Distortion in Sintering

During sintering a powder compact gains strength through low-temperature interparticle bonding, usually induced by solid-state surface diffusion, followed by further strength contributions from high-temperature densification. In cases where a liquid phase forms, sintering densification is accelerated and shape retention is sustained while open pores remain and contribute capillary forces. Unfortunately, sintering densification requires the compact become thermally softened to a point where creep strain rates reach levels near 10(exp -2)/s when the liquid forms. On the other hand, thermal softening of the powder compact substantially reduces the strength at high temperatures. Therefore, the in situ strength evolution during sintering is a primary focus to separate compact densification (as required for high performance) with minimized distortion (as required for net-shaping). With respect to gravitation effects on distortion during sintering there are two points of substantial weakness - prior to significant interparticle bonding and during final pore closure. This research is focused on understanding the competition among interparticle neck growth, densification, thermal softening, grain boundary wetting, capillary effects associated with liquid wetting and residual porosity, and gravity. Most surprising is the apparent role of gravity, where the deviatoric stress acting on the powder structure induces skeletal formation that reduces distortion. In contrast with theory, microgravity samples exhibit more distortion yet fail to fully densify. Results are presented on the experimental concepts supporting an emerging model of sintering strength evolution that enables understanding of both distortion and densification. The experiments have relied on tungsten heavy alloys, various combinations of dihedral angle, pore size, initial porosity, liquid:solid ratio, and heating rates. On Earth, the dominant factor with respect to distortion is the starting body heterogeneity. Current modeling efforts are seeking some means to uniformly predict the distortion based on a starting pore structure heterogeneity parameter. Densification is largely unaffected by the initial pore structure, but distortion is highly variable, suggesting that nonuniform pore closure might be a significant parameter during sintering. With respect to flight experiments, plans include removal of the solid body forces acting on the solid grains, allowing stabilization of the pore structure and examination of the buoyancy effects with regard to distortion. In microgravity there is the surprising result that compacts do not densify, yet distort a factor that is contrary to all current sintering models. Densification without distortion during liquid phase sintering was achieved by manipulating microstructure and its evolution during sintering. Microstructure parameters such as the solid volume fraction, dihedral angle, initial porosity, and pore size were varied to measure densification and distortion behavior during LPS using W-Ni-Cu alloys. Green compacts were formed using ethylene-bis-stearamide as a pore-forming agent with the amount of polymer controlling the initial porosity. Different initial pore sizes were generated by varying the polymer particle size. Dihedral angle was varied by changing the Ni:Cu ratio in the alloys. Finally, the solid volume fraction was adjusted via the tungsten content. Distortion was quantified using profiles determined with a coordinate measuring machine to calculate a distortion parameter. Sintering results showed that solid volume fraction and dihedral angle are the dominant factors on densification and distortion during liquid phase sintering. Distortion decreases with increasing solid volume fraction and dihedral angle, while initial porosity and pore size have no observable effect on distortion at nearly full densification. Various strategies emerge to improve distortion control in liquid phase sintering.

German, Randall M.↗

DOE OSTI2025

First multi-institutional systematic comparison of the neutron ambient dose equivalent produced by proton therapy systems

Objective. Isochronous cyclotrons, synchrocyclotrons, and synchrotrons are used to accelerate protons for proton therapy. An accurate measurement of neutron doses generated by these accelerators and associated delivery systems and its clinical relevance requires systematic protocols and proper neutron dosimetry for a meaningful assessment. We present the first comprehensive comparison of neutron ambient dose equivalent (H*(10)) produced by clinically operational proton therapy systems. Approach. Treatment plans with 10 cm modulation-depth and ranges of 10 cm (R10M10) and 25 cm (R25M10) were created to cover a 10 × 10 × 10 cm 3 water target. The pencil beam scanning proton therapy machines studied were: two gantry-mounted synchrocyclotrons (Hyperscan, Mevion, half-gantry), two isochronous cyclotrons (ProBeam, Varian, full-gantry), one isochronous cyclotron (Proteus, IBA, full-gantry), and two synchrotrons (PROBEAT, Hitachi, full- and half-gantry). Proton beams were delivered to 30 × 30 × 40 cm 3 plastic water phantoms. WENDI-II and LUPIN-BF3-NP neutron rem-meters were positioned at three angles (0°, 45°, 90°) relative to the beam direction to measure the neutron H*(10) at distances between 50–300 cm from the isocenter. Main results. H*(10) showed dependence on beam energy, machine type, and measurement location. The highest reading was for the gantry-mounted synchrocyclotron, whereas other systems produced approximately comparable neutron doses. In all cases, the H*(10) reduced with distance from the isocenter. The H*(10) drop at 2 m distance compared to that at 0.5 m was a factor of ∼5 for the gantry-mounted synchrocyclotron whereas in other systems the decrease was a factor of 10. The WENDI-II device suffered from dead-time-associated under-estimation of the dose by a factor of ∼2–3 under the synchrocyclotron beam due to its high dose-per-pulse. However, WENDI-II and LUPIN-BF3-NP results were within reasonable agreement in isochronous cyclotron and synchrotron beams, indicating that both devices are suitable for those systems. Significance. Neutron H*(10) is dependent on various parameters including beam energy, measurement location, as well as machine design. Caution must be exercised in choosing the appropriate neutron-dose-measurement device to be used for low-duty-factor, particularly in high-instantaneous-rate proton delivery systems. By delivering the same volumetric proton dose across different machines, this work provides a benchmark for inter-system comparisons and serves as a foundation for future studies.

LUPIN↗

DOE OSTI2025

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Certification of tactics and strategies in aviation

The paper suggests that the 'tactics and strategies' notion is a highly suitable paradigm to describe the cognitive involvement of human operators in advanced aviation systems (far more suitable than classical functional analysis), and that the workload and situational awareness of operators are intimately associated with the planning and execution of their tactics and strategies. If system designers have muddled views about the collective tactics and strategies to be used during operation, they will produce sub-optimum designs. If operators use unproven and/or inappropriate tactics and strategies, the system may fail. The author wants to make a point that, beyond certification of people or system designs, there may be a need to go into more detail and examine (certify?) the set of tactics and strategies (i.e., the Operational Concept) which makes the people and systems perform as expected. The collective tactics and strategies determine the information flows and situational awareness which exists in organizations and composite human-machine systems. The available infrastructure and equipment (automation) enable these information flows and situational awareness, but are at the same time the constraining factor. Frequently, the tactics and strategies are driven by technology, whereas we would rather like to see a system designed to support an optimized Operational Concept, i.e., to support a sufficiently coherent, cooperative and modular set of anticipation and planning mechanisms. Again, in line with the view of MacLeod and Taylor (1993), this technology driven situation may be caused by the system designer's and operator job designer's over-emphasis on functional analysis (a mechanistic engineering concept), at the expense of a subject which does not seem to be well understood today: the role of the (human cognitive and/or automated) tactics and strategies which are embedded in composite human-machine systems. Research would be needed to arrive at a generally accepted 'planning theory' which can elevate the analysis, description and design of tactics and strategies from today's cottage industry methods to an engineering discipline. The available infrastructure and equipment (automation) enable these information flows and situational awareness, but are at the same time the constraining factor. Frequently, the tactics and strategies are driven by technology, whereas we would rather like to see a system designed to support an optimized Operational Concept, i.e., to support a sufficiently coherent, cooperative and modular set of anticipation and planning mechanisms. Again, in line with the view of MacLeod and Taylor (1993), this technology driven situation may be caused by the system designer's and operator job designer's over-emphasis on functional analysis (a mechanistic engineering concept), at the expense of a subject which does not seem to be well understood today: the role of the (human cognitive and/or automated) tactics and strategies which are embedded in composite human-machine systems. Research would be needed to arrive at a generally accepted 'planning theory' which can evaluate the analysis, description and design of tactics and strategies from today's cottage industry methods to an engineering discipline.

Koelman, Hartmut↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗