Abstract: The hinge joint is an important and fragile component of assembled hollow-slab bridges. Therefore, it is necessary to regularly identify hinge joint damage for guaranteeing the safety of assembled hollow-slab bridges. ...

Abstract: Machine learning (ML)–based data-driven approaches have become increasingly prevalent for predicting structural performance. Because a properly trained ML model can learn hidden patterns in databases of experimental samples, ...

Abstract: Data-driven approaches based on machine learning (ML) have become progressively popular for the shear capacity prediction of reinforced concrete (RC) deep beams because the fine-tuned ML models could mostly outperform the ...

Abstract: A probabilistic model is devised for predicting the plastic hinge lengths (PHLs) of RC columns. Seven existing parametric models are evaluated first using a comprehensive database comprising PHL measurements from 133 RC ...

Abstract: The capabilities of data-driven models based on machine learning (ML) algorithms in offering accurate predictions of structural responses efficiently have been demonstrated in numerous recent studies. However, efforts to ...

Abstract: Highway bridges are vital infrastructure engineering whose safety severely influences the safety and stability of society. Damage identification, as the core part of structural health monitoring, plays an essential role ...