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This study aims to investigate the process of adjustment for international construction professionals when facing new technical contexts. It introduces a framework called construction technical intelligence (CTI) and seeks to provide valuable insights and practical guidance for professionals involved in international construction projects.

In this study, a grounded theory approach was employed, which included conducting in-depth interviews with 28 professionals engaged in international construction projects. The qualitative data gathered from these interviews underwent systematic analysis to identify important categories and develop theoretical perspectives.

The findings demonstrate the framework of CTI, which encompasses four essential dimensions that play a significant role in facilitating the successful adjustment of international construction professionals to new technical contexts. These dimensions underscore the multidimensional nature of CTI and offer valuable insights into the necessary capabilities for professionals to thrive in dynamic and globalized construction environments.

By proposing this comprehensive framework, the study contributes to the knowledge and understanding of the technical adjustment process for international construction professionals. It also establishes a foundation for future quantitative research to validate and refine the proposed model, enabling a deeper comprehension of the dynamics involved in professionals' technical adjustment.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.