This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick estimate for building costs, hence improving operational efficiency and competitiveness of a construction company. A dataset composed of 10,000 parametric building configurations, collected from end-to-end real-world activities in our partner company, was used to train and validate the ML models to perform multiple tasks. Among the 13 ML regression algorithms used, the Artificial Neural Network (ANN), Gradient Boosting, and XGBoost models appear to be the most suitable to estimate the building costs and the required resources with an accuracy of 99% within less than a second of the training time. The ANN models are also developed to identify available options of the building features under a given budget. The optimization problem under constraints is solved, helping clients determine the optimal building costs according to their preferences. Besides, the optimized building costs obtained by this study are 7% smaller than those of the actual data, hence to improve the company’s competitiveness. This study showcases that ML models can be efficiently used in the construction sector to optimize the workflow for cost savings and provide some practical implications for data-driven management.

Effects of processing parameters on preheated (heat-assisted) clinching process to join aluminum alloy 5052-H32 (AA5052) and thermoplastic carbon-fiber-reinforced-plastic (TP-CFRP) sheets for cross-tension (CT) specimens were first studied. Preheating was critical since brittle TP-CFRP could be softened to avoid fracturing or cracking during clinching process. Four processing parameters, including punching force, die depth, heating mode, and heating temperature, were considered. Quasi-static tests and microscope observations were taken to evaluate AA5052/TP-CFRP clinch joints in CT specimens and determine appropriate processing parameters for fatigue tests. Finally, fatigue data and failure mode of clinch joints in CT specimens were obtained and discussed.