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/ An Improved Hybrid XGBoost Model for Culvert Inspection Using Swarm Intelligence Algorithms https://trid.trb.org/View/2329210 Wed, 27 Mar 2024 16:52:50 GMT https://trid.trb.org/View/2329210 The control method for ship tracking when navigating through narrow and curved sections https://trid.trb.org/View/2351101 Wed, 27 Mar 2024 16:52:36 GMT https://trid.trb.org/View/2351101 Travel Time Prediction for Congested Freeways With a Dynamic Linear Model https://trid.trb.org/View/1895339 Wed, 27 Mar 2024 16:51:50 GMT https://trid.trb.org/View/1895339 Empirical Study on Intercity Logistics Distribution Demand Forecast Based on Grey-Markov Model https://trid.trb.org/View/2282468 Wed, 27 Mar 2024 11:52:26 GMT https://trid.trb.org/View/2282468 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 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 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 In-service performance assessment of fire-corrosion damaged cables of bridges https://trid.trb.org/View/2301954 Tue, 26 Mar 2024 17:11:41 GMT https://trid.trb.org/View/2301954 Bayesian-optimized interpretable surrogate model for seismic demand prediction of urban highway bridges https://trid.trb.org/View/2310928 Tue, 26 Mar 2024 16:02:42 GMT https://trid.trb.org/View/2310928 Development of Pressure Pulsation 1D Model for Brake System using “2 pressures/2 systems” Method https://trid.trb.org/View/2329062 Fri, 22 Mar 2024 17:05:45 GMT https://trid.trb.org/View/2329062 A Parametric Thoracic Spine Model Accounting for Geometric Variations by Age, Sex, Stature, and Body Mass Index https://trid.trb.org/View/2341914 Fri, 22 Mar 2024 17:04:53 GMT https://trid.trb.org/View/2341914 Validity across four common street-crossing distraction indicators to predict pedestrian safety https://trid.trb.org/View/2334501 Fri, 22 Mar 2024 17:04:52 GMT https://trid.trb.org/View/2334501 Towards reliable seismic fragility assessment of highway bridges with oblong columns considering the drift-based capacity directionality effect https://trid.trb.org/View/2292906 Fri, 22 Mar 2024 09:22:44 GMT https://trid.trb.org/View/2292906 A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost https://trid.trb.org/View/2343497 Fri, 22 Mar 2024 09:22:44 GMT https://trid.trb.org/View/2343497 Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure https://trid.trb.org/View/2344931 Fri, 22 Mar 2024 09:22:44 GMT https://trid.trb.org/View/2344931