Experimental Research Method

The experimental research method is a quantitative approach designed to discover the effects of presumed causes. The key feature of this approach is that one thing is deliberately varied to see what happens to something else, or to discover the effects of presumed causes. This approach can be used in both laboratory settings and field settings. A Field Experiment “is an experimental research study that is conducted in a real-life setting. The experimenter actively manipulates variables and carefully controls the influence of as many extraneous variables as the situation will permit”. A Laboratory Experiment “is a study that is conducted in the laboratory and in which the investigator precisely manipulates one or more variables and controls the influence of all or nearly all of the extraneous variables”.

The experimental approach has the primary advantage of being able to infer causal relationships. However, it is easier to identify causal description, which describes the consequences of deliberately varying a treatment, than it is to achieve a causal explanation, which clarifies the mechanisms by which a causal relationship holds. A second advantage of the experiment is that it controls for the influence of extraneous variables. Other advantages are that it permits the precise manipulation of one or more variables, produces lasting results, suggests new studies, and suggests solutions to practical problems. The experimental approach has the disadvantages of not being able to test for the effects of nonmanipulated variables, creating an artificial environment, and frequently being time consuming and difficult to design.

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In a field setting, the author makes use of a real-life situation and thereby avoids criticism for having created an artificial environment. Typically, however, there is not as much control over extraneous variables. In a laboratory setting, the experimenter brings the participants into the laboratory, where there is maximum control over extraneous variables; however, this usually means creating an artificial environment.

Two Laboratory Experiments and two Field Experiments had been conducted to validate the approaches proposed in this thesis. More specifically, Laboratory Experiment 1 was to evaluate the performance of the proposed DNA-based constraint modelling method in LNG construction; Laboratory Experiment 2 was to evaluate the performance of the proposed TCM method. Field Experiment 1 and 2 was to validate the effectiveness and efficiency of the selected tracking technologies (i.e. barcode for offsite fabrication tracking, GPS for shipping and delivery tracking, and Active RFID for construction site logistics tracking).

(1) Laboratory Experiment

The two Laboratory Experiments were conducted based on a LNG Construction Simulation

Game. Game-based experiments (Camerer and Fehr 2004) had been widely used to set up

situations for strategic interaction and test many of the fundamental assumptions of construction management theory such as lean and pull planning. Most games involve anonymous agents, physical tools, real small-scale buildings (made by woods or plastics), and paper-based instructions or drawings. Players cannot communicate with each other unless allowed. Conventions often favour minimal and vague description of the game to the subjects when recruited, the use of private spaces where the game is explained with a standard script, the inclusion of a question and answer session and sometimes a test is administered to ensure subjects understand the game (Jackson 2011). In the game the players make choices according to the information they have in their hands such as construction progress, resource constraints, and permit issues.

Tommelein et al. (1999) used a Parade Game to evaluate the impact of work flow variability on trade performance. The game consists of simulating a construction process in which resources produced by one trade are prerequisite to work performed by the next trade. Perng et al. (2006) utilised a Bidding Game to explore the bidding situation for economically most advantageous tender projects. 24 participants played the game and the results revealed that the game had the potential to identify important factors in the bidding situation, simulate competitive bidding behaviours, and explore competitive advantages in the bidding process. Sacks and Goldin (2007) and Sacks et al. (2007) had successfully tested lean construction concepts on high-rise apartment buildings through a lean simulation game named LEAPCON.

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Van-den-Berg et al. (2017) developed a serious gaming approach and applied it to train students to learn how to improve the performance of a construction supply chain.

LNG Construction Simulation Game

This is a pre-existing game that is used to demonstrate LNG construction process including procurement and supply chain. Construction works within this game include: site preparation, module installation (the modules are manufactured off-site), pipework installation, wiring installation, and major equipment installation. Figure 3-2 illustrates the detailed construction work flows. Engineering constraints in this game include engineering drawings and instructions (as shown in Figure 3-3); Supply-chain constraints include materials, instruments, and off-site fabricated modules (as shown in Figure 3-4); and Site constraints include tools, preceding works, permits, work space, workforce, and safety (as shown in Figure 3-5). Table 3-2 shows the number of people and roles needed to play the game.

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Figure 3-4: Materials, Instruments, and Off-site Fabricated Modules

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Table 3-2: The Roles of the People in the LNG Construction Simulation Game

Roles Quantity Project Manager 1 Plant Manager 1 Engineering Manager 1 Procurement Manager 1 Site Manager 1 Module Manufacturing 6 Civils Contractor 2 Mechanical Contractor 1 Pneumatic Contractor 2 Electrical Contractor 1 Major Equipment Installation 1

Shipping 1

Commissioning 1

Total 20

Participants

Theoretically, a large enough random sample of males and females provides the best basis for generalising results over the general population and avoiding a gender bias (Järvelä 2014). However, in practice this goal is often problematic to achieve. Although many women study construction management courses and work for LNG construction, male population still accounts for the vast majority. Therefore, acquiring comparable numbers of experiment participants of both genders with good sample size can sometimes be difficult. Similarly, it is hard to conduct an experimental study that would have enough participants in each age group to provide statistically significant results without limiting the amount of relevant variables through participant selection. Instead, these factors have to be taken into account when analysing the data, interpreting the results, and generalising them (Järvelä 2014).

For Laboratory Experiment 1, the aim is to evaluate the performance of the proposed DNA- based constraint modelling method in LNG construction. Therefore, participants with basic construction management knowledge are required. 20 students from a construction management course were randomly selected based on their Student Identifications. The majority of participants were male and around 20 years old (75% male and 25% female). For Laboratory Experiment 2, the aim is to evaluate the performance improvement of the

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proposed TCM method compared with conventional approach. Therefore, two groups are needed. All the 40 participants were graduate students (i.e. same knowledge level) and had very limited knowledge on lean and TCM. Meanwhile, there were both male and female subjects (82.5% male and 17.5% female), which aligned to the real project team. The participants were randomly split into two groups: Group A with TCM implementation and Group B without. In order to reduce the learning curve issues, there was a basic training session (30 minutes) for both groups, and they were also guided to play once before starting the test. Two additional players were assigned to represent the clients of the LNG project. One selected design variations at regular time intervals through the game and delivered them to the project manager; the other checked completed tasks and issued permits to site managers.

Data Collection and Analysis

The raw experimental data was collected by (1) video recorders, and (2) predefined worksheets (i.e. module quality reports, progress reports, and commissioning reports). These data were then processed for further statistical analysis and interpretation based on predefined indicators. In Laboratory Experiment 1, five indicators were developed to analyse the performance of constraint removal during the experiment, such as Number of Unconnected Components at time i (NUCi), Variance of Constraint Removal at time i (VCRi), Variance of IWP Released to Site at time i (VIRSi), Out-Degree of a Constraint Node (ODCN), and In-Degree of a CWP/IWP Node (IDCN/IDIN). Detailed explanation of each indicator, experiment design, and experiment results can be found at Chapter 5.

In Laboratory Experiment 2, indicator of Cumulative Progress (CP) was developed to measure the actual progress at the end of each time interval; and productivity indicator was defined to measure the performance of each construction trade. Other indicators such as the number of defective LNG modules, and actual duration were also defined and calculated. Detailed explanation of each indicator, experiment design, and experiment results can be found at Chapter 8.

(2) Field Experiment

Field Experiment 1 was conducted at a real fabrication facility owned by Fremantle Steel

Group. Barcode technology was deployed to a small batch of products to track its fabrication processes including cutting, drilling, assembly, welding, surface treatment, pre-assembly, and despatch. Field Experiment 2 was conducted at a real LNG plant facility owned by the Australian Centre for Energy and Process Training (ACEPT). GPS and Active RFID were deployed to track the shipping process and construction site logistics, respectively. Detailed experiment design of these two field experiments are explained in Section 7.4, Chapter 7.

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Data Collection

Two types of data were collected during the two experiments. The first one was the sensor data. In Field Experiment 1, Barcodes were scanned manually by workers as required and uploaded to a 3D-based data platform which was developed by the researcher. In Field

Experiment 2, GPS tags were read by the satellite (i.e. no human needed). Active RFID tags

were read by the fixed readers installed on the test bed. Data from GPS and RFID were also sent to the 3D-based data platform for data integration and visualisation. The second type of data was secondary data that collected from Fremantle Steel Group and ACEPT. These data included historical fabrication data (i.e. time spent for generating progress reports, time spent for progress checking, time spent for locating missing components, etc.), norms, site maps, and facility details.

Data Analysis

For Field Experiment 1, the secondary data such as site maps and facility details were interpreted by the researcher so as to design an overall tracking scenario. Data generated from barcodes included the scanned time and locations. The location data was firstly transformed to the status of each tracked object such as “in cutting and drilling”. Then, the status information together with the scanned time were transformed to progress information. Finally, Microsoft Project tool was utilised to generate S-Curves (i.e. accumulated progress curves with normal distribution) for project progress monitoring and measurement. The efficiency of the barcode-based fabrication tracking solution was measured based on the time and cost reduction analysis compared with historical project information.

For Field Experiment 2, RFID signals were received by the four Fixed RFID Readers at a predefined period. The locations of these RFID tags were determined through triangulation calculations. In order to assess the accuracy of the active RFID system, a performance analysis of the RFID tags localisation was conducted. Given that the magnitude of Radio Signal Strength (RSS) was related to the distance between reader and tag, the researcher first validated the relationship of RSS between each RFID tags in the simulated LNG plant construction environment. Two randomly selected RFID tags were put at the same position on a trolley located within the detection range of the four fixed readers. The results showed that the RSS distributions of the dynamic cases were more fluctuated than that of the static cases. However, both tags at the same place responded different RSS values but the patterns of changes were similar with each other. It suggested that there was a relationship between RSS responses and the distances of RFID tags, which could be utilised to further improve measurements as long as it could be formed. Once the tags with known locations were obtained as reference tags, the

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measured location of the target tag was calibrated by the RSS responses from those tags through the determined relationship.