Three-dimensional TLS is a data acquisition technology that has been adopted in various industries to aid in reducing costly errors and improving efficiency. Three-dimensional TLS captures geospatial information of an area and is used for a wide range of applications such as archaeology (Al-Kheder et al., 2009), Building Information Management (BIM) development of as-built models, topographic mapping, heritage conservation, geomorphology, structural monitoring, and quality control (Kim et al., 2013). The scanner has a laser that rotates at high speeds and emits a pulse of laser light waves to a target or area and measures the distance to the target by timing the round-trip time of the pulse of light (Bhatla et al., 2012). A completed area scan creates a digital representation of that area. Scans are needed from multiple locations to capture all aspects of the objects (Kawashima et al., 2014). This collection of data points is used to generate dimensionally accurate as-built information and is becoming standard practice in engineering design (Omar and Nehdi, 2016). The scans are used to develop 3D computer-aided design (CAD) models to design piping systems (Safa et al., 2015).

Previous research has quantified these benefits. For example, TLS-based surveys have demonstrated dimensional accuracy in the range of 2–6 mm, which depends on scanning distance, compared to traditional tape-based or manual measurement errors that can exceed 20 mm in complex industrial environments (Aryan et al., 2021; Wardius and Hein, 2024). In addition, TLS has been shown to reduce field data collection time by more than 50%, significantly improving project efficiency while reducing safety risks associated with scaffolding and manual verification (Wu et al., 2021). These quantitative improvements provide a compelling case for TLS adoption in piping design.

Piping design requires dimensional accuracy for efficient pipe routing and connections (Nguyen and Choi, 2018). Designers create digital 3D models as part of the design process and the current practice of site as-built data acquisition relies heavily on manual observations (Kim et al., 2013). 3D models aid in improving designs before being built in the field (Becerik-Gerber et al., 2011). 3D models provide realistic visualizations of the final piping design and are used to convey design intent to the construction personnel prior to and during field installation (Rabbani et al., 2007). Terrestrial laser scanning helps minimize human error from the field data collection process and the data are collected efficiently and effectively over a shorter period compared to traditional methods (Aryan et al., 2021). The collected data can then be relayed to the project team faster. Terrestrial laser scanning provides an alternative to traditional piping design and offers many benefits in dimensional accuracy and improved measurement capability (Muralikrishnan, 2021).

Decision support tools are used in a variety of industries to make informed decisions by providing structured guidance to make effective decisions (Bagstad et al., 2013; Cooke et al., 2008; Cronemyr et al., 2001). Attia et al. (2013) presented an energy-oriented software tool that facilitated the decision-making process of zero energy buildings. There was a need for the evaluation of biofuel production pathways in the energy sector and a framework for a DST was developed by Perimenis et al. (2011). The cement industry requires the implementation of the most appropriate energy efficiency solutions with the use of DSTs (Mokhtar and Nasooti, 2020). These tools, whose output may vary according to the user’s inputs, may suggest the ideal decision direction (Rose et al., 2016).

Literature reviews were conducted to identify previous research on the use of 3D TLS and later validate ideas from the researched sources of information. This review is based on 15 selected articles from six journals within the domain of automation in construction, construction engineering and project management. The articles were searched using key phrases such as “piping design” and “terrestrial laser scanning.” The articles were examined to extract common themes, benefits, and challenges associated with 3D TLS.

The overall purpose of using semi-structured interviews for data collection is to gather information from key respondents who have professional experiences, attitudes, perceptions and beliefs related to use of TLS for piping design (Dejonckheere and Vaughn, 2019). A semi-structured interview helps gain more insight to understand the needs or viewpoints needed to develop the decision support tool (Kallio et al., 2016). Semi-structured interviews were used to determine key words and common themes based on the interviewees’ responses (Dicicco-Bloom and Crabtree, 2006). This interview structure fosters learning about individual experiences and perspectives (Dicicco-Bloom and Crabtree, 2006). Fisher et al. (2017) used semi-structured interviews with healthcare general practitioners to understand perceptions of provider workloads. Pre-determined open-ended questions are asked to get independent ideas and thoughts from the respondents and used to supplement the other methods in this paper’s triangulation approach (Adams, 2015). A framework is needed to develop an interview guide which considers the common themes derived from the literature review.

Kallio et al. (2016) produced a framework for the development of a semi-structured interview guide. The steps involved are as follows: (1) identifying the prerequisites to use a semi-structured interview; (2) retrieving previous knowledge from literature reviews; (3) formulating of the preliminary interview guide by creating a list of interview questions by reviewing past observations and identifying gaps in the piping design process; (4) pilot testing; (5) presenting the complete interview guide. Guiding questions including ‘grand tour’ questions, core questions and planned and unplanned follow-up questions are shown in Table 1 (Spradley, 2016). Questions related to the common literature review themes were developed and asked during the interview.

Thematic analysis is a widely used qualitative analytic method (Roulston, 2001). Boyatzis (1998) describes thematic analysis as a tool to use across multiple methods. Braun and Clarke (2006) describe the benefits of thematic analysis and the flexibility it gives as a research method. Braun and Clarke (2006) outline a 6-phase guide to performing thematic analysis shown in Table 2. Following completion of the semi-structured interviews, a qualitative data analysis software application, NVivo 12, will be used to code and perform a thematic analysis of the responses to the interview questions.

Simple additive weighting (SAW) is a weighted linear combination or scoring method. It is one of the most often used multi-criteria decision techniques (Afshari et al., 2010). This technique is aimed at determining a weighted score for each alternative by adding the contribution of each attribute multiplied by their weights. The steps of SAW include the following: (1) weighting of criteria (Delphi method), (2) obtaining the criteria/decision matrix, (3) calculating the normalized decision matrix, (4) multiplying the normalized weight of each criterion with respective criteria weighting to yield a ranking of the alternatives (Afshari et al., 2010).

The Delphi method is a technique used to obtain consensus of opinion from a panel of experts within a particular field (Toepoel and Emerson, 2017). This technique involves defining and recruiting experts who possess knowledge in a particular area, who are representatives in the specific discipline, or who have credible relevant professional experience (Hsu and Sandford, 2007; Williams and Webb, 1994). Several advantages of using the Delphi method include the following: time and cost effectiveness, discussion of complex problems, participants have time to synthesize their ideas and have applications in many fields (Akins et al., 2005). The weighting of criteria will be determined by this expert panel.

The expert panel size can vary. An accurate procedure does not exist for identifying the number of panel experts in any individual study (Williams and Webb, 1994). According to Dalkey et al. (1969), the panel size can have an effort on accuracy and reliability has shown to increase linearly between 3 and 11 experts with accuracy improving up to a maximum of 29. If the backgrounds of the participants are homogeneous, studies have shown that a panel of 10–15 experts could be efficient (Hsu and Sandford, 2007). Boulkedid et al. (2011) conducted a systematic review of the Delphi method and found that the minimum number of experts on a panel to be three and the median number to be 17. For this study, the panel of experts consisting of design engineers, design supervisors, project managers, and engineering managers provides homogeneous backgrounds in piping design and terrestrial laser scanning.

Construction of a decision matrix will include two methods (TLS and traditional) and criteria. The scale of values in Table 3 will be used to identify the intensity of importance for the comparison (Afshari et al., 2010). Normalizing the decision matrix creates a standardization of criteria which helps facilitate the comparison, which will be finally followed by the evaluation of the score of each alternative with respect to each weighted criterion.

To enhance clarity and usability, a visual representation of the decision support tool (DST) is provided in Figure 3. This figure illustrates the process flow from input criteria selection to final ranking using the SAW method with Delphi-derived weights.

To demonstrate the use of the DST, a case study was conducted which involved a piping design project at an industrial plant site in southeast Louisiana. The site area is in an operating unit in a 9 × 12-m area. The project scope includes approximately 72 m of 150-millimeter (mm) diameter pipe at multiple elevations with four tie points into existing piping. The DST will be used by a piping designer with 17 years of experience at the site. Demonstration of the tool is based on the criteria weights resulting from the Delphi method, normalization of the decision matrix, and final ranking to determine the better method.

Studies have uncovered the effectiveness and usefulness of laser scanning for dimensional quality control (Bhatla et al., 2012). Al-kheder et al. (2009) developed a 3D documentation system for palaces in the Jordan desert using 3D laser scanning. Panella et al. (2020) assessed the benefit of laser scanning in rail tunnel inspections. 3D laser scanning technology’s widespread adoption in applications across various industries continue to grow as the value and benefits are realized (Park et al., 2007). Bosché et al. (2015) used technology for automated comparison of as-built and as-planned cylindrical mechanical, electrical and plumbing components including piping installation. Greaves and Hohner (2009) presented research findings resulting in the benefits of applying 3D laser scanning technology to the design, construction, and operation of industrial plants. Terrestrial laser scanning has been considered as the best available technology to capture 3D information due to its speed and dimensional accuracy (Bosché et al., 2015). Piping design and construction in industrial plants require efficiency and dimensional accuracy (Nguyen and Choi, 2018).

Although these studies show that there are several applications of using TLS in various industries, there are inherent limitations of these studies which does not include a methodology or decision support tool that is useful in deciding whether to use TLS as a data acquisition tool in piping design rather than using conventional methods of measurement (Soudarissanane et al., 2011; Yen Kin et al., 2014). However, overall benefits included reduction of errors, improved planning and design, improved work processes, improved safety, improved quality, enhanced collaboration, project readiness, and accommodation of scope changes. These benefits were used as a starting point for a decision support tool criterion, which includes the following common themes that were used to develop questions in the semi-structured interview stage of this research paper: time savings, cost effectiveness, accuracy and precision, detailed documentation, and safety.

Pilot testing was performed using the interview guide with 12 industry professionals with an average of 22 years of experience in piping design. Common themes used to develop questions in the semi-structured interview included the following: dimensional accuracy, safety factors (environmental, less risk in the field), repeatability, cost effective, reduced costs associated with access (scaffolding, personnel lifts), reduced field time. No additional questions were required from the initial pilot testing since there were no new questions based on the participants’ responses. The pilot testing was followed by seven additional semi-structured interviews to compile additional data on common themes resulting from the interviews. The results of the responses and themes are outlined in the next section.

The thematic analysis from the semi-structured interview questions yielded five common themes. Those themes included the following: personnel safety, time savings, dimensional accuracy, cost effectiveness, and repeatability. These common themes were consistent with the results and findings of the literature review. The common themes were used as individual criteria as part of the decision matrix as shown in Table 4.

The multi-criteria decision analysis (MCDA) method used in this study was the simple additive weighting (SAW) method. In this SAW approach, the Delphi method was used to determine the criteria weightings. The Delphi panel consisted of 12 industry professionals with substantial expertise in areas directly relevant to piping design and terrestrial laser scanning (TLS). Panelists were selected to provide a balance of perspectives from design engineers, project managers, engineering managers, and design supervisors, each with an average of more than 20 years of experience in industrial plant projects. Importantly, the panel included representatives from both industrial plant operations and third-party engineering service providers, ensuring diversity in professional backgrounds and organizational perspectives. This selection strategy provided credibility to the weighting process by incorporating insights from practitioners with extensive field experience and strategic decision-making responsibilities.

The experts were given a questionnaire to respond with a weighting for each of the five criterion resulting from the thematic analysis (see Table 4). The initial questionnaire started with an equal weight of 0.20 for each criterion. The total weighting of all the criterion would have to equal 1.0 (increments of 0.05). The results were averaged, and then a second questionnaire with those results was shared with the experts. There were two rounds conducted to reach consensus on the weight of each criterion. The sum of the weights should be equal to one. Table 5 shows the results of weightings for each criterion. The experts agreed that time savings were the most important criterion followed by dimensional accuracy.

To demonstrate the proposed tool, a case study was conducted involving a typical piping design project at an industrial plant site. The approximate size of the operating unit was 9 × 12-m area. The project scope includes installation of new 150 mm diameter piping approximately 72 m in length that spans multiple elevations. The new piping will tie into four existing piping systems. The DST was used by a piping designer with 17 years of experience at the site. Step 1 includes use the of the criteria matrix and the weights determined by the Delphi method (Table 5). For Step 2, the piping designer determines the intensity of importance for each criterion based on his/her personal experience and scope of the project for each alternative which is the use of TLS or traditional process. Results of the importance scores are shown in Table 6.

Step 3 normalizes the decision matrix to give a meaning comparison between the criteria (Table 7).

Step 4 evaluates each alternative with respect to the criteria and weight. The sum is then used to rank the two alternatives. Table 8 shows the results and ranking. In this case study, use of TLS ranked higher than the traditional piping design process.

A brief sensitivity analysis to test the impact of a different variable assumption regarding the intensity of importance for the time savings criterion is presented in Table 9. With the changes in the time savings intensity of importance scores from one to five, the use of TLS ranked higher in each scenario. Notably, the difference between the sum for each method was closer with the lower time savings intensity of importance scores for TLS. The intensity of importance scores for other criterions for either TLS or traditional methods can yield a different outcome or ranking depending on the expert judgement of the individual assigning the scores.