Abstract Effective roadside vegetation management is vital for safety, visibility, and infrastructure longevity. Traditional methods are often labor-intensive, time-consuming, and difficult to scale. This paper presents a novel approach leveraging light detection and ranging (LiDAR) data and geospatial analysis for large-scale roadside vegetation management. We introduce the vegetation-free volume (VFV), a spatial boundary defining clear roadside space. Using high-resolution LiDAR point clouds and advanced geospatial processing, we detect and quantify overgrown vegetation encroaching into the VFV. The method transforms point clouds into a homogeneous coordinate system, applies geometric queries to identify breaches, and generates horizontal and vertical density profiles to visualize overgrowth. Structured machine- and human-readable reports provide actionable insights for automated systems and field crews. Furthermore, we simulate vegetation growth and pruning cycles at the LiDAR-point level to support maintenance scheduling and resource optimization. Results demonstrated that integrating LiDAR and geospatial analysis enhances precision, efficiency, and scalability in roadside vegetation management.
Abstract The adoption of three-dimensional concrete printing (3DCP) has been transforming the construction industry, offering unique design flexibility and efficiency that conventional methods struggle to match. Despite its potential, a critical lack of research exists for nonmaterial categories, including management, cost estimation, sustainability, and the absence of standardized guidelines. This study presents a comprehensive review of 3DCP in the context of digital construction, applying a methodological framework to assess the current state of research. A total of 76 review papers were selected and analyzed across seven thematic categories: general technology overview (GTO), materials and mechanical properties (MMP), reinforcement and structural performance (RSP), printers and printing processes (PPP), sustainability (SUS), application and case studies (ACS), and guidelines, standards, and codes (GSC). The review studies showed a significant concentration on MMP (38%) and GTO (29%), but other essential categories, such as SUS (7%) and GSC (0%), received minimal to no attention. Taking this further, a research guidance matrix consisting of four analytical dimensions, namely, current landscape, research objectives, challenges and gaps, and measurable outcomes, was developed based on a comprehensive mapping review of 1,627 peer-reviewed articles. The analysis revealed that MMP (89.67%) and PPP (88.69%) are the most dominant topics in the literature. Based on the findings, targeted future research directions are proposed for each category. In conclusion, this study bridges existing knowledge gaps and provides actionable recommendations for researchers and practitioners to further enhance 3DCP as a viable, sustainable, and scalable construction method capable of transforming the construction industry.
Abstract The point cloud of an arch dam comprises two key target categories: the dam body and the bedrock. The bedrock exhibits complex, variable structures and anisotropic surface textures, presenting significant differences from the artificially constructed arch dam. To fully capture the distinctive features of both target types and enhance the registration accuracy of the arch dam, this paper proposes a deep learning-based point cloud registration method driven by arch dam contour features. This approach first addresses the substantial differences in surface features between dam body and rock mass by proposing a contour feature extraction method based on the transformer framework and collaborative clustering. This method separately acquires the contours of both the dam body and rock mass. Subsequently, to overcome the information loss during dimensionality reduction of high-dimensional features by traditional multilayer perceptron, which leads to estimation errors in the transformation matrix and causes local misalignment or global offset in registration results, this paper calculates 12-dimensional feature vectors from the positional relationships between points as input to the registration network. A channel attention mechanism combining average, max, and random pooling is constructed to extract deeper point cloud features from the arch dam data. Finally, a rigid transformation estimation network computes the transformation matrix. The proposed contour feature extraction method captures richer details for registration. Ablation experiments demonstrate that incorporating the feature extractor reduces rock mass and dam registration errors from 24.08 and 23.9 m to 0.71 and 0.64 m, respectively. Following the introduction of the multipooling attention mechanism, the errors were further reduced to 0.0181 and 0.0169 m, respectively. The proposed method provides a robust data foundation for detailed three-dimensional modeling and high-precision deformation detection.
Abstract Probabilistic stability analysis of soil slopes is essential for reliable geotechnical design due to pronounced uncertainties in soil strength and hydraulic parameters. Although crude Monte Carlo simulation (MCS) provides a robust benchmark, its prohibitive computational cost and the limitations of gradient-based methods such as FORM and SORM in handling nonlinear and non-Gaussian performance functions restrict their practical applicability. To address these challenges, this study conducts a comparative analysis of advanced surrogate-based reliability methods, specifically polynomial chaos expansion–enhanced Monte Carlo simulation (PCE-MCS) and kriging-enhanced Monte Carlo simulation (K-MCS), against traditional gradient-based methods such as the first-order reliability method (FORM) and second-order reliability method (SORM) for evaluating the stability of unsaturated finite soil slopes. The crude MCS serves as the benchmark. The work examines the predictive accuracy and computational efficiency of these methods in estimating the probability of failure ( p f ). Parametric studies demonstrate nonlinear variations in p f with mean and coefficient of variation of stability number ( c / γ H ), internal friction angle ( ϕ ), and fitting parameters of soil-water characteristic curve (SWCC) ( a f / γ H , n f , and m f ). In terms of computational efficiency, surrogate-assisted approaches provide orders-of-magnitude reductions in wall-clock time compared to crude MCS. When normalized with respect to PCE-MCS (unity), K-MCS requires only 1.85 times the computational cost, whereas FORM/SORM requires approximately 28 times. In contrast, crude MCS incurs computational costs exceeding four orders of magnitude for large sample sizes. Statistical testing confirms that stochastic methods (K-MCS, PCE-MCS) significantly outperformed gradient-based methods (FORM, SORM). Although no statistically significant difference is observed between K-MCS and PCE-MCS ( p -value = 0.83), K-MCS exhibited slightly narrower confidence intervals and lower mean error (0.0116 versus 0.0153), indicating marginally greater robustness. Moreover, K-MCS maintains point-estimate error percentages below 8% across most scenarios, with PCE-MCS below 11%. In contrast, FORM showed maximum errors of up to 50.67%, followed by SORM with errors as high as 31.97%. The study also includes Sobol’ sensitivity analysis to identify the influence of each input parameter on the factor of safety. Comparisons of Sobol’ indices derived using post-processed PCE, PCE-MCS, and K-MCS reveal consistent results, with friction angle ( ϕ ) identified as the most influential parameter (total-order Sobol’ index = 0.66). To facilitate practical application, the study concludes with developing design charts using kriging for target reliability indices of 3.0. These charts provide an important tool for slope stability assessment, enabling precise and efficient evaluations under unsaturated soil conditions. The findings emphasize the efficacy of PCE-MCS and K-MCS as robust and computationally efficient surrogate modeling techniques for quantifying the probability of failure of unsaturated soil slopes and facilitating reliability-based design optimization of finite slopes.
Abstract Aging infrastructure with limited budgets has driven stakeholders to explore new methods for monitoring structural health. Structural health monitoring represents a strategy to address this challenge using physical measurements to characterize performance. This approach has often leveraged various sensing modalities to describe operational response to loading environment, with most strategies relying on continuous measurement. In recent years, the complexity and economic constraints associated with continuous monitoring have motivated alternatives. The digital twin framework offers a model-first approach, where real-world data informs virtual models (the “twin”), enables dynamic updates based on measurements and further offers enhanced assessment and predictive capabilities. This work formulates a graph neural network-powered prototype digital twin of a structural system that can identify unseen damage through updating intact finite element models of small-scale representations of structural elements based on full-field measurements. Within this study, MeshGraphNet is trained on noisy strain and displacement data using noise statistics characterized from a physical digital image correlation experimental setup, which differs from most existing methods that rely on idealized or synthetically generated noise. Three models were trained on noise-free data, data with noise, and data with improved signal-to-noise ratios. Results demonstrate that model performance correlates positively with damage severity, achieving an F1 score of 43.79% for the damaged class across the data set. Performance improved substantially when evaluated on specific damage severity ranges, reaching 78.75% for instances with 45%–50% damage, as shear strain exceeds the noise floor at higher damage severities. When compared to robust principal component analysis and denoising autoencoders, the proposed mixed data training strategy outperforms these preprocessing methods. Validation on a physical dogbone specimen demonstrates consistent performance across simulated and experimental data, indicating potential for real-world implementation. Future work aims to extend the framework to more extensive experimental data and assess the scalability of the trained model for larger and more complex structural systems.
