| The deterioration of civil infrastructures is a serious problem for society and a considerable challenge for civil engineers. To alleviate and prevent such degradation, manual inspections are carried out for continuous monitoring and records are saved in structural inventory databases. Structural damage prediction models that use an integration of statistical and artificial intelligent algorithms were developed based on an evidential database extracted and organized from a structural inventory database. Damage in the abutment walls of highway bridges was used as an example problem where the goal was to identify the sources of damage and predict their structural condition with time. Unbalance, complexity, subjectivity, and incompleteness are major deficiencies in the evidential database, which obstruct the development of prediction models. Novel data organizing schemes were thus developed to overcome these obstacles and make full use of the database in the training of an ensemble of neural networks. The damage identification performance of such an ensemble of networks reached 86%, which exceeded the performance the best-trained single networks in the ensemble by 18%. Also contributing to the development of successful prediction models were other soft computing methods, statistical analyses, field inspection and monitoring, and finite element analyses. A virtual database was created through finite element analyses to analyze the behavior of structures with different design parameters. The combination of an evidential database and a virtual database in ensembles of neural networks was found to be a promising innovation to improve the performance of the developed damage prediction models. |
