# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 5 Conclusions  
This study quantitatively evaluated the economic and productivity-enhancing effects of incorporating TLS into structural safety assessment processes. Three actual case projects were selected and comparatively analyzed, contrasting traditional manpower-based methods with the TLS-based 3D scanning reverse modeling method. Queueing models (M/M/1 and M/D/1) were applied to determine key performance indicators, including service rates, server utilization, and average waiting times, forming the basis for cost and ROI evaluations.  
The results demonstrate that the TLS-based method achieved significantly higher service rates compared to the visual inspection method. In particular, rapid scanning and data integration with BIM substantially reduced inspection durations and the personnel required. Project-level analysis revealed that the largest-scale project (Project B) achieved a high ROI of 135.88%, whereas the relatively smaller-scale Projects A and C recorded lower ROIs of 13.81% and 12.12%, respectively. Nevertheless, the cumulative ROI at the company level from executing all three projects consecutively reached 161.8%, confirming substantial long-term economic advantages from investing in TLS.  
Practically, this research highlights that TLS implementation enables shorter inspection periods, reduced costs, and the standardization of operational processes in structural safety diagnosis. Although there is an initial investment cost burden, empirical evidence demonstrates that sufficient returns on investment can be secured through continuous and repetitive project execution over the long term. Academically, this study contributes by quantitatively analyzing the labor-intensive characteristics and performance variability inherent to construction inspection using queueing theory, offering theoretical support for enhancing production system efficiency.  
However, this study has limitations, including an analysis restricted to three case projects conducted by a single company, and the exclusion of certain cost components, such as equipment depreciation, software licensing, and personnel training. Consequently, the conclusions regarding long-term ROI may be somewhat optimistic if such factors significantly raise the total expenditure. Additionally, the diversity in construction project types (e.g., tunnels, bridges, skyscrapers) and scales was not addressed. Therefore, future research should involve a broader variety of cases and comprehensively incorporate all cost elements, including depreciation and life-cycle costs, to clarify the broader impact of digital transformation across the construction industry. Further integration and application of various digital technologies, such as AI, IoT, and unmanned aerial vehicles (UAVs), to enhance structural safety assessment efficiency could potentially extend the applicability and economic benefits of digital technologies.  
Conceptualization, K.W. and J.-j.K.; methodology, T.K. and K.W.; software, T.K. and K.W.; validation, T.K., K.W. and N.H.; formal analysis, T.K.; investigation, T.K.; resources, T.K. and N.H.; data curation, T.K. and N.H.; writing--original draft preparation, T.K. and K.W.; writing--review and editing, N.H. and J.-j.K.; visualization, T.K.; supervision, J.-j.K.; project administration, T.K. and N.H.; funding acquisition, T.K. and N.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (IRIS RS-2025-00557724).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.