Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2024. National Academy of Sciences. All rights reserved. http://blogs.law.harvard.edu/tech/rss tris-trb@nas.edu (Bill McLeod) tris-trb@nas.edu (Bill McLeod) Transport Research International Documentation (TRID) https://trid.trb.org/Images/PageHeader-wTitle.jpg https://trid.trb.org/ Rating of constructed timber bridges repaired with steel beams https://trid.trb.org/View/2330310 Wed, 27 Mar 2024 16:52:17 GMT https://trid.trb.org/View/2330310 A simplified simulation strategy for barge-bridge collision learned from collapse of Taiyangbu Bridge https://trid.trb.org/View/2335452 Wed, 27 Mar 2024 16:52:17 GMT https://trid.trb.org/View/2335452 Shear strength of self-compacting concrete dry joints subjected to combined axial, bending and shear forces in precast concrete segmental bridges https://trid.trb.org/View/2330307 Wed, 27 Mar 2024 16:52:17 GMT https://trid.trb.org/View/2330307 Numerical study on the dynamic amplification factors of highway continuous beam bridges under the action of vehicle fleets https://trid.trb.org/View/2339007 Wed, 27 Mar 2024 16:52:17 GMT https://trid.trb.org/View/2339007 Vulnerability assessment and lifecycle analysis of an existing masonry arch bridge https://trid.trb.org/View/2321977 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2321977 Experimental study on a cable-stayed bridge isolated with the combination of elastoplastic cables and fluid viscous dampers in the transverse direction https://trid.trb.org/View/2320553 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2320553 Monitoring bearing damage in bridges using accelerations from a fleet of vehicles, without prior bridge or vehicle information https://trid.trb.org/View/2320502 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2320502 Ship collision performance of a flexible anti-collision device designed with fiber-reinforced rubber composites https://trid.trb.org/View/2320087 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2320087 Study on vortex-induced vibration mitigation of parallel bridges by multi-tuned mass damper inerter https://trid.trb.org/View/2317628 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317628 A machine learning based method for predicting the shear strength of Fiber-Reinforced Concrete joints in precast segmental bridges https://trid.trb.org/View/2317633  GBDT > RF > KNN > SVM > Lasso. The best-performing XGBoost model achieved performance metrics of R2 = 0.964, MAE = 41.318 kN, RMSE = 56.760 kN, and MAPE = 14.228%. The superior reliability of the XGBoost ensemble model was further validated through comparisons with current design codes and existing models. As ML-based models are typically black-box models, the SHapley Additive exPlanations (SHAP) method was employed to explicitly link the predicted output to the inputs. Based on SHAP results, confining stress (CS), depth of shear key (D), compressive strength of concrete (fcu), total height of joints (H), and height of flat part (Hsm) recognized as the crucial parameters affecting ultimate load.]]> Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317633 Prescriptive maintenance of prestressed concrete bridges considering digital twin and key performance indicator https://trid.trb.org/View/2317615 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317615 Development of a seismic vulnerability and risk model for typical bridges considering innovative intensity measures https://trid.trb.org/View/2317608 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317608 Seismic fragility of circular piers in simply supported RC bridges: A proposal for capacity assessment https://trid.trb.org/View/2317599 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317599 Estimating design positions of suspension bridge tower saddles in the completed bridge state: An analytical approach https://trid.trb.org/View/2296836 Tue, 26 Mar 2024 17:11:41 GMT https://trid.trb.org/View/2296836 Rapid peak seismic response prediction of two-story and three-span subway stations using deep learning method https://trid.trb.org/View/2296749 Tue, 26 Mar 2024 17:11:41 GMT https://trid.trb.org/View/2296749