Enhancing strategic investment in construction engineering projects: A novel graph attention network decision-support model

Abstract

Selecting the right investment projects is a pivotal decision-making process that can steer a company’s financial and operational future. Existing methods often fall short in merging machine learning with network-based multi-criteria decision-making (MCDM) strategies. This research presents a first-time investment network framework fed into a graph attention network (GAT) to forecast the success of construction engineering projects by leveraging their interrelated data across various decision-making parameters. Expert judgment was initially employed to filter over 33,000 investment projects based on organizational goals, project risk, and business development ratings. The refined dataset was organized into three specialized MCDM investment-decision networks: regional-based, country-level, and funding-mode-based. These networks were subsequently fed into GAT models to classify investment values. The regional-based network achieved over 99 % accuracy, the country-level and funding-mode-based networks delivered over 98 % accuracy. These insights demonstrate that while all three models maintain high accuracy, the slight variances in their performance reflect the importance of tailoring decision-support tools to specific geographical contexts. The understanding of different network structures can provide strategic decision-making insight for large-scale infrastructure investments, where even minor misclassifications can lead to substantial financial consequences.