civil engineering firms

Abstract: Probabilistic hazard assessment of geophysical mass flows requires large simulation ensembles and thus fast and robust numerical models. We present an open-source depth-averaged material point method formulated in global Cartesian coordinates, using Lagrangian particles to track the flow margin. A separate basal friction step based on the Coulomb, Voellmy, or $μ(I)$-rheology brings the flow to rest without artificial stopping criteria. Our model reproduces analytical solutions for a sliding block and dam-break problems and matches the deposit of a laboratory granular column collapse. A back-calculation of the 2023 Brienz/Brinzauls rockslide (Switzerland) at 1m resolution (190'000 particles) runs faster than real time on a single CPU core. Calibrated to the observed runout, the three friction laws yield very similar results, but Coulomb friction shows a slightly wider affected area than Voellmy and $μ(I)$. A probabilistic 1632-simulation ensemble at 5m resolution was computed in 76s. The model is one to two orders of magnitude faster than established depth-averaged models used in engineering practice, making catchment-style ensemble simulations feasible on a desktop computer.

Abstract: Automated building design must comply with seismic and wind codes and satisfy structural mechanics constraints, yet most existing agents produce visually plausible models without verification grounded in mechanical analysis and code compliance. We introduce the Vibe Building task and propose PE-Loop (Physics-Engine-in-the-Loop), an agent in which a deterministic physics engine is the sole source of evaluation signals, mapping code constraints to a physics process reward, while the language model is confined to proposing discrete revisions (section menu, topology, and lateral system). Designs are verified by held-out seismic and wind time-history checks and a constructability gate. On VB-Bench, 3,577 physics-adjudicated building instances across six code families, PE-Loop achieves the highest verified success rate under three of four backbone LLMs, the highest held-out seismic pass rate under all four, and the highest held-out wind pass rate under three. Replacing the physics verdict with a language-model judge, all else fixed, leaves 58.43% of accepted designs noncompliant. These results suggest that reliable structural design rests less on a stronger LLM proposer than on an adjudicator the proposer cannot influence, a division of labor for agents whose outputs must hold up in the physical world.

Abstract: This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata from large-scale aerial LiDAR. The pipeline begins with semantic segmentation of the input point cloud using an improved KPConv-based model, in which data sampling and loss functions are adapted to emphasize pole and conductor (wire) classes. Network topology inference then proceeds in two stages: (i) pole instances are obtained by clustering pole-class points and validating candidates using geometric criteria, including height and verticality estimated via PCA, and (ii) candidate pole pairs are evaluated using a heuristic method and a lightweight ResNet-based classifier on 2D top-view projections of pole and wire point distributions to determine whether a physical conductor span exists. By explicitly classifying candidate spans, the approach mitigates common failure modes of heuristic connectivity rules in dense or cluttered scenes and under partial wire observation. For each validated wire, attributes regarding utility infrastructure geometry are computed, including endpoint conductor heights, ground elevation, sag-related lowest-point features, conductor arrangement, and wire width. Evaluation on multiple real-world aerial LiDAR datasets demonstrates decimeter-level endpoint height accuracy and approximately 9% relative improvement in recall for topology reconstruction compared to heuristic nearest-neighbor baselines, with larger gains in complex layouts.