| Unsupervised machine learning for robust bridge damage detection: full-scale experimental validation E Akintunde, SE Azam, A Rageh, DG Linzell Engineering Structures 249, 113250, 2021 | 27 | 2021 |
| Singular value decomposition and unsupervised machine learning for virtual strain sensing: Application to an operational railway bridge E Akintunde, SE Azam, DG Linzell Structures 58, 105417, 2023 | 11 | 2023 |
| Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges S Ardani, E Akintunde, D Linzell, SE Azam, Q Alomari Engineering Structures 286, 116096, 2023 | 6 | 2023 |
| Probabilistic bridge fatigue evaluation at virtual sensing locations using kernel density estimation E Akintunde, SE Azam, DG Linzell International Journal of Fatigue 176, 107885, 2023 | 5 | 2023 |
| Experimental study of continuous-beam lateral torsional–buckling resistance with a noncomposite concrete deck CS Sun, DG Linzell, JA Puckett, E Akintunde, A Rageh Journal of Structural Engineering 148 (4), 04022026, 2022 | 4 | 2022 |
| Full scale bridge damage detection using sparse sensor networks, principal component analysis, and novelty detection E Akintunde, SE Azam, A Rageh, D Linzell Proceedings 42 (1), 2019 | 4 | 2019 |
| Application of proper orthogonal decomposition to bridge damage detection-field investigations S Ardani, E Akintunde, D Linzell, SE Azam Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and …, 2022 | 1 | 2022 |
| Noncomposite concrete deck bracing effects on continuous steel stringer lateral torsional buckling resistance CS Sun, OF Babarinde, D Kuruppuarachchi, DG Linzell, JA Puckett, ... Engineering Structures 283, 115886, 2023 | | 2023 |
| Fatigue Prognosis in Bridges Using Long Term Monitoring Data EO Akintunde The University of Nebraska-Lincoln, 2022 | | 2022 |
Daniel LinzellAssoc. Dean, Grad & Int’l Pgms, College of Engineering, Univ. of Nebraska-LincolnAdresă de e-mail confirmată pe unl.edu