Abstract Use of Tuned Mass Dampers (TMDs) to reduce the wind-induced torsional response of structures has been investigated in the literature by assuming that the wind excitations can be approximated by harmonic forces or white noise random processes; however, such an assumption is not realistic. Further, wind load effects are correlated. This study is focused on the effectiveness of different linear/nonlinear TMDs configurations to reduce the wind induced response. For the analyses, the structure is modeled as a multi-degree of freedom system under correlated wind load effects. The results show that the selection of optimum dampers is affected by the consideration of the correlated wind load effects.

The use of Artificial Neural Networks (ANN) is explored to predict peak ground accelerations (PGA) and pseudospectral acceleration (SA) for Mexican inslab and interplate earthquakes. A total of 277 and 418 seismic records with two horizontal components for inslab and interplate earthquakes, respectively, are used to train the ANN models by using an ANN with a feed-forward architecture with a back-propagation learning algorithm. Both ANN with single and two hidden layers are considered. For comparison purposes, the PGA and SA values predicted by the trained ANN models are compared with those estimated with attenuation relations or ground motion prediction equations (GMPEs). The comparison indicates that the predicted PGA and SA values by the trained ANN models, in general, follow the trends predicted by the GMPEs. However, an extensive verification of the trained models must be conducted before they can be used for seismic hazard and risk analysis since, on occasion, the PGA and SA values predicted by the trained ANN models depart from the behaviour observed from the actual records.

El uso de redes neuronales artificiales es explorado para predecir aceleraciones máximas del terreno y pseudoaceleraciones para sismos de tipo intraslab e interplaca. Un total de 277 y 418 registros sísmicos de dos componentes para sismos de intraslab e interplaca, respectivamente, son usados para entrenar los modelos de las redes neuronales artificiales con alimentación hacia adelante y con un algoritmo de aprendizaje de retroalimentación. Se consideran redes neuronales artificiales con una y dos capas ocultas. Con fines de comparación, valores de aceleración máxima del terreno y pseudoaceleración predichos con los modelos de las redes neuronales son comparados con los estimados mediante relaciones de atenuación o relaciones de movimiento fuerte. La comparación indica que los valores predichos, en general, siguen la tendencia de los valores obtenidos con las relaciones de movimiento fuerte. Sin embargo, se debe llevar a cabo una verificación extensa de los modelos entrenados antes que estos puedan emplearse en análisis de peligro y riesgo sísmico ya que, en ocasiones, los valores predichos no reflejan el comportamiento observado de los registros.