The predictive performance of machine learning (ML) models is challenged when trained on class imbalance real-world construction datasets, reducing the accuracy of relevant decisions. In construction projects, the collection of a balanced dataset is not always feasible. Here, the integration of generative and prediction models holds potential, synthesizing the underrepresented class and configuring a balanced input dataset. This study improves the performance of construction prediction models through the integration of a generative model that augments the dataset for the underrepresented class. For this, a variational autoencoder (VAE) was integrated into a multi-head graph attention network (GAT), whereby a comprehensive construction productivity dataset was collected across different projects related to different construction activities, each with a particular structure and level of class imbalance. Balancing the class distribution led to a significant increase in the predictive performance of the GAT model, where accuracy jumped from 90.6 % to 92.5 %, 81.1 % to 94.4 %, and 92.2 % to 95.4 % when trained on finishing, concrete, and insulation activity networks, respectively.

Abstract Rapid detection of damages occurring as a result of natural disasters is vital for emergency response. In recent years, remote sensing techniques have been commonly used for the automatic categorization and localization of such events using satellite images. Trained based on natural disaster images, a convolutional neural network (CNN) has been applied as a highly successful method, with its ability to reveal outstanding features. Studies aiming to detect target points obtained as a result of extracting visual features from natural images within these networks have achieved their goals. In this study, ensemble learning methods have been suggested as a means to develop the detection of landslide areas from landslide satellite images. Landslide image dataset has been trained for their categorization in CNN models and then they have been used again to localize landslide regions. While model predictions develop overall performance and status, different ensemble strategies have been used and integrated to reduce the sensitivity to prediction variance and training data. Class-selective relevance mapping (CRM) has been used to visualize individual CNN models and ensemble learned behaviors. As a result of the comparisons made based on mean average precision metrics and the criteria of intersection over union, model ensembles have proved to show higher localization performance than any other individual model.