# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 2 Materials and Methods
### Economic Analysis of TLS-Based 3D Scanning Reverse Modeling  
The introduction of digital technology in the construction industry is not just a matter of verifying the technical feasibility of new equipment or software but must also be followed by an economic feasibility assessment from the perspective of corporate investment decisions. The following methodologies are typically used. A cost-benefit analysis (CBA) compares and analyzes the expected benefits and costs of technology adoption in monetary terms [35; 59]. Life cycle cost (LCC) is a method of evaluating by calculating all costs incurred during the life cycle of a facility from the planning stage to the disposal stage [60; 61]. It is mainly used to analyze the economics of modular construction [62] and energy-saving [63; 64], but can also be applied to estimate the long-term costs and benefits of adopting construction technologies [65]. Return on investment (ROI) is a basic indicator used to evaluate investment performance as the ratio of profit from an investment divided by the cost of the investment [66; 67; 68]. Existing economic evaluation methodologies are macro-evaluation methodologies, and they are limited in analyzing resource efficiency and process changes due to changes in detailed work processes caused by the introduction of digital technology for structural safety diagnosis. To supplement this, it is necessary to analyze changes in the detailed work processes of structural safety diagnosis before and after the introduction of digital technology and to simulate the process at the stage before performing an economic analysis for the adoption of new technology. In the construction industry, CYCLONE [69; 70], RESQUE [70], and STROBOSCOPE [71] and various simulation techniques have been developed to pursue workflow and resource optimization. Previous studies have focused on the movement or placement of resources, making it easy to treat the entire process from a macro-perspective. On the other hand, the application of queueing theory is effective for analyzing the construction production system from a microscopic perspective and quantitatively understanding resource allocation issues, especially production system optimization [72], resource efficiency [73], and process optimization [74]. The queueing model models the interaction process between the customer (e.g., work, quantity) and the server (e.g., labor, equipment) through a probabilistic or deterministic distribution from the perspective of \"arrival-waiting-service\" [75]. This allows for the intuitive identification of bottlenecks in the process of various fields [76] and the establishment of resource allocation strategies [77; 78]. By quantifying the waiting time and service time that occur at points where the process does not proceed smoothly [78], project managerscan accurately identify insufficient resources and decide on additional deployment. It is also advantageous for comparing and analyzing productivity changes before and after the introduction of a technology transition with a relatively constant work rate. In the construction sector, research using queuing models has been conducted steadily. There are studies where the number of concrete pump trucks was optimized [79], where BIM RFI data were considered as a customer to evaluate the service rate of personnel [80], or where the optimal number of pouring teams was calculated when pouring foundation concrete [81]. These studies prove that the queueing theory is useful for reducing unnecessary waiting time at construction sites and minimizing service costs and construction period delays. In particular, as the speed of work processing in construction projects decreases with the spread of digitalization, the value of using distribution methods, such as the M/D/1 model of queueing, is increasing. Therefore, this study will compare and analyze the detailed work processes before and after the introduction of digital technology, and based on this, a quantitative simulation will be conducted using a queueing model.