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PurposeThe purpose of this study was to investigate and propose the appropriate K‐mapping models as an approach to integrating key project components and technologies for the effective improvement of project performance within and across construction projects.Design/methodology/approachIn this holistic, single‐case study, one of the largest construction consulting firms in South Korea has been studied by conducting 15 semi‐structured interviews and the different loci for each of the K‐mapping components are identified and analyzed. Based on the different loci, four types of the K‐mapping model are provided and elucidated.FindingsResearch findings indicate that these four types of the K‐mapping model provide the criteria to identify the appropriate types of K‐map for construction project organizations, according to the characteristics and conditions of their own construction personnel, construction processes, and K‐transfer technologies. With the K‐mapping models, an appropriate knowledge management system (KMS) can be developed more effectively.Research limitations/implicationsFirst, as interpretivism was adopted as the research philosophy, the case study findings were subjective and qualitative to both the interviewees in the case study company and the researchers, though this study provided an important underpinning for future research on K‐mapping within construction project organizations. Second, the theory developed in this study was based on an investigation of the appropriate K‐mapping models with only a single case study. Nevertheless, this case study provided sufficient data and information to develop and propose a theory for successful K‐mapping model development within construction project organizations.Originality/valueIn the KM area, the definition, benefits, purposes, principles and types of K‐map have been already provided by many KM researchers and practitioners. However, no industry (practical)‐based K‐mapping model has been developed and proposed, especially in the construction industry. Accordingly, the originality of this study to be presented in one of the paper's conclusions: construction processes must be considered and adopted as a key component in the K‐mapping process, and the discussion of the four types of K‐map this research have generated, which significantly expands the existing literature on K‐mapping.

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The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%–85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%–73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%–95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.