arXiv subjects

Mohamad Alipour

Publications and source records attributed to Mohamad Alipour.

12 recordsLinked to original sources

arXiv2026

Finite Element Model Updating-based Load Rating of Bridges with Incomplete As-Built Information

Load rating is the engineering process of determining the safe load-carrying capacity of an existing bridge structure often through analysis of its load-bearing members and their cross sections. However, when structural drawings and details of the structure are missing or insufficient for such calculations, alternative solutions must be used to infer the capacity. This study proposes a rational and consistent engineering solution for capacity inference based on structural identification (St-Id). The proposed approach uses finite element model updating (FEMU) to estimate the unknown characteristics of structures for use in an analytical load rating, which requires the development of an initial model developed in ABAQUS that can be updated based on experimental test data. ABAQUS allowed for the development of an interface with MATLAB, which facilitated the integration of an automatic iterative parameter optimization algorithm. The optimization algorithm developed in this investigation incorporated the features of a genetic algorithm (GA) and a gradient-based scheme to iterate on the unknown parameters. The proposed approach was evaluated on four in-service highway bridges including two concrete slab and two T-beam structures in varying deterioration condition states, which had sufficient plans available but were treated as having varying degrees of unknown details. The results illustrated that the finite element (FE) model updating approach generated load ratings that were within 0-17% of the target load ratings while alleviating the main challenges of the existing alternatives.

cs.CE↗

arXiv2026

Automated Gaze-based Behavioral Segmentation and Temporal Representation for Bridge Inspection in Unconstrained 3D Environments

Visual bridge inspection is a knowledge-intensive task in which inspectors coordinate visual search, spatial navigation, structural reasoning, and defect identification and documentation. It is a central maintenance task for bridges and a key basis for safety assessments, yet its results are susceptible to individual subjectivity. While eye-tracking-based behavioral studies quantify underlying processes, existing research often imposes restrictive simplifications to reduce environmental complexity, thereby compromising ecological validity. This study proposes an automated data analytics framework for converting multimodal inspection data into an inspection mode time series. Unconstrained 3D gaze, head-movement, drone navigation, and scene geometry data are segmented into temporal windows and classified into three functional modes: global scanning, local inspection, and navigation. The resulting temporal representation enables the extraction of interpretable behavioral descriptors, including transition probabilities, dwell times, transition entropy, fixation measures, and spatial revisit metrics. A feasibility study using a virtual bridge inspection platform demonstrates that the proposed representation captures meaningful differences in inspection strategy and reveals exploratory relationships with inspection performance. This study contributes a framework for human-informed computer-aided infrastructure inspection systems, inspector training, and data-driven assessment of constructed facilities.

cs.HC↗

arXiv2026

GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar

Soil moisture is a critical variable for managing irrigation, improving crop yield, and understanding field-scale hydrology. Radars mounted on unmanned aerial vehicles (UAVs) offer a promising means to monitor soil moisture over large fields with flexible, high-resolution coverage. However, during the growing season, canopy scattering and soil reflections become strongly coupled in the radar measurement. These coupled effects vary with crop structure or flight altitude, complicating the retrieval of soil moisture. To overcome this challenge, we present GreenScatter, a physics-based soil moisture retrieval framework for nadir-looking wideband UAV radars. GreenScatter introduces a microwave radiative transfer model that explicitly captures the dominant electromagnetic interactions between vegetation and soil, enabling accurate modeling of coherent ground backscatter through canopy. In parallel, it develops a radar cross-section (RCS) estimation method that transforms time-domain radar signals into calibrated wideband RCS spectra, isolating soil reflections while compensating for hardware and waveform effects. Together, these components enable robust soil moisture estimation through vegetation across varying canopy conditions and UAV configurations. Field experiments across multiple corn and soybean sites demonstrate consistent retrieval with an average volumetric water content (VWC) error of 4.49%.

eess.SP↗

arXiv2025

Bridging simulation and reality in subsurface radar-based sensing: physics-guided hierarchical domain adaptation with deep adversarial learning

Accurate estimation of subsurface material properties, such as soil moisture, is critical for wildfire risk assessment and precision agriculture. Ground-penetrating radar (GPR) is a non-destructive geophysical technique widely used to characterize subsurface conditions. Data-driven parameter estimation methods typically require large amounts of labeled training data, which is expensive to obtain from real-world GPR scans under diverse subsurface conditions. A physics-based GPR model using the finite-difference time-domain (FDTD) method can be employed to generate large synthetic datasets through simulations across varying material parameters, which are then utilized to train data-driven models. A key limitation, however, is that simulated data (source domain) and real-world data (target domain) often follow different distributions, which can cause data-driven models trained on simulations to underperform in real-world scenarios. To address this challenge, this study proposes a novel physics-guided hierarchical domain adaptation framework with deep adversarial learning for robust subsurface material property estimation from GPR signals. The proposed framework is systematically evaluated through the laboratory tests for single- and two-layer materials, as well as the field tests for single- and two-layer materials, and is benchmarked against state-of-the-art methods, including the one-dimensional convolutional neural network (1D CNN) and domain adversarial neural network (DANN). The results demonstrate that the proposed framework achieves higher correlation coefficients R and lower Bias between the predicted and measured parameter values, along with smaller standard deviations in the estimations, thereby validating their effectiveness in bridging the domain gap between simulated and real-world radar signals and enabling efficient subsurface material property retrieval.

eess.SP↗

arXiv2025

Continuous subsurface property retrieval from sparse radar observations using physics informed neural networks

Estimating subsurface dielectric properties is essential for applications ranging from environmental surveys of soils to nondestructive evaluation of concrete in infrastructure. Conventional wave inversion methods typically assume few discrete homogeneous layers and require dense measurements or strong prior knowledge of material boundaries, limiting scalability and accuracy in realistic settings where properties vary continuously. We present a physics informed machine learning framework that reconstructs subsurface permittivity as a fully neural, continuous function of depth, trained to satisfy both measurement data and Maxwells equations. We validate the framework with both simulations and custom built radar experiments on multilayered natural materials. Results show close agreement with in-situ permittivity measurements (R^2=0.93), with sensitivity to even subtle variations (Delta eps_r=2). Parametric analysis reveals that accurate profiles can be recovered with as few as three strategically placed sensors in two layer systems. This approach reframes subsurface inversion from boundary-driven to continuous property estimation, enabling accurate characterization of smooth permittivity variations and advancing electromagnetic imaging using low cost radar systems.

eess.SP↗

arXiv2025

UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization

High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual channel algorithm (MTDCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TB distributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation against in situ measurements indicates that PoLRa derived SM retrievals from the SCAV and MTDCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3 across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around 0.02 m3/m3 and 0.11 m3/m3 for SCA and DCA related retrievals.

eess.SP↗

arXiv2024

Sub-Meter Remote Sensing of Soil Moisture Using Portable L-band Microwave Radiometer

Spaceborne microwave passive soil moisture products are known for their accuracy but are often limited by coarse spatial resolutions. This limits their ability to capture finer soil moisture gradients and hinders their applications. The Portable L band radiometer (PoLRa) offers soil moisture measurements from submeter to tens of meters depending on the altitude of measurement. Given that the assessments of soil moisture derived from this sensor are notably lacking, this study aims to evaluate the performance of submeter soil moisture retrieved from PoLRa mounted on poles at four different locations in central Illinois, USA. The evaluation focuses on the consistency of PoLRa measured brightness temperatures from different directions relative to the same area, and the accuracy of PoLRa derived soil moisture. As PoLRa shares many aspects of the L band radiometer onboard the NASA Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual channel algorithm were applied to calculate soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts. In each test period, the standard deviations of observed dual polarization brightness temperatures are generally less than 5 K. By comparing PoLRa based soil moisture retrievals against the moisture values obtained by handheld time domain reflectometry, the unbiased root mean square error and the Pearson correlation coefficient are mostly below 0.04 and above 0.75, confirming the high accuracy of PoLRa derived soil moisture retrievals and the feasibility of utilizing SMAP algorithms for PoLRa data. These findings highlight the significant potential of ground or drone based PoLRa measurements as a standalone reference for future spaceborne L band sensors.

eess.SP↗

arXiv2024

Bayesian inversion of GPR waveforms for sub-surface material characterization: an uncertainty-aware retrieval of soil moisture and overlaying biomass properties

Accurate estimation of sub-surface properties such as moisture content and depth of soil and vegetation layers is crucial for applications spanning sub-surface condition monitoring, precision agriculture, and effective wildfire risk assessment. Soil in nature is often covered by overlaying vegetation and surface organic material, making its characterization challenging. In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire risk assessment. This study thus proposes a Bayesian model-updating-based approach for ground penetrating radar (GPR) waveform inversion to predict moisture contents and depths of soil and overlaying material layer. Due to its high correlation with moisture contents, the dielectric permittivity of both layers were predicted with the proposed method, along with other parameters, including depth and electrical conductivity of layers. The proposed Bayesian model updating approach yields probabilistic estimates of these parameters that can provide information about the confidence and uncertainty related to the estimates. The methodology was evaluated for a diverse range of experimental data collected through laboratory and field investigations. Laboratory investigations included variations in soil moisture values, depth of the overlaying surface layer, and coarseness of its material. The field investigation included measurement of field soil moisture for sixteen days. The results demonstrated predictions consistent with time-domain reflectometry (TDR) measurements and conventional gravimetric tests. The depth of the surface layer could also be predicted with reasonable accuracy. The proposed method provides a promising approach for uncertainty-aware sub-surface parameter estimation that can enable decision-making for risk assessment across a wide range of applications.

eess.SP↗

arXiv2024

Joint Soil and Above-Ground Biomass Characterization Using Radars

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.

eess.SP↗

arXiv2024

Dual-Frequency Radar Wave-Inversion for Sub-Surface Material Characterization

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed for predicting the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors.

eess.SP↗

arXiv2024

FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping.

eess.IV↗

arXiv2023

UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization

Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.

cs.RO↗