Recent small-scale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions? (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion? and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data? We address these questions by developing a mechanism for incremental training of semantic segmentation models. We call the resulting extension task-specific model updates (TSMU). A national compilation of landslide inventories by the U.S. Geological Survey (USGS) was used to develop a global database for this study. Our results indicate that the TSMU framework can be used to aid in the creation of new landslide inventories or expanding existing datasets, and also to rapidly develop hazard maps for situational awareness following a widespread landslide event.

Understanding and predicting process–structure–property–performance relationships for concrete materials is key to designing resilient and sustainable infrastructure. While machine learning has emerged as a powerful tool to supplement empirical analysis and physical modeling, its capabilities are yet to be fully realized due to the massive data requirements and generalizability challenges. To address these limitations, we propose a knowledge‐informed machine learning framework for concrete property prediction that aggregates the wealth of domain knowledge condensed in empirical formulas and physics‐based models. By integrating the knowledge through data augmentation, feature enhancement, and model pre‐training, we demonstrate that this framework has the potential to (i) accelerate model convergence, (ii) improve model performance with limited training data, and (iii) increase generalizability to real‐world scenarios (including extrapolation capability to other datasets and robustness against data outliers). The overall improvement of machine learning models by knowledge integration is particularly critical when these models are scaled up to tackle the increasing complexity of modern concrete and deployed in practical applications. While demonstrated for predicting concrete strength, this versatile framework is applicable to a wide range of properties of concrete and other composite materials, paving the way for accelerated materials design and discovery.

Pile foundations are often subjected to lateral loadings. By adding steel plates (fins) to the side of the pile, steel fin pile foundations (SFPFs) can improve the lateral load capacity of piles. Existing numerical studies using elastic–perfectly plastic models (e.g., Mohr–Coulomb model) for soil behavior may result in unrealistic load response when modeling soil–pile interaction in dense sand due to the lack of consideration of strain-hardening/softening behavior. In the present study, finite-element analyses were conducted to understand the effect of fin geometry on the lateral load capacity of SFPFs. The prepeak hardening and postpeak softening soil behavior was accounted for by varying soil strength parameters with plastic shear strain based on a modified Mohr–Coulomb model. The developed model was calibrated and validated against well-documented experimental and field tests in the literature. The validated model was subsequently used to conduct a parametric study to understand the effect of fin geometry on the response of SFPFs subjected to lateral loading at the pile head. The behavior of SFPFs at different displacement levels and load levels was studied. The effect of the relative density of soil on the performance of SFPFs was also investigated. Based on the numerical simulation results, the optimal fin width and length values for mobilizing soil resistance were suggested and the underlining mechanisms affecting the efficiency of fins were discussed.