Select a real-world dataset in which an identifiable problem that wouldbenefit from a data analytics solution exists. Develop a data-driven model for thisproblem and demonstrate how the model is used to support enhanced decision-making processes for those involved in this application.
- The proper topics are construction management, infrastructure systems, disaster management, housing, urban planning.
Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination Computing in Civil Engineering 2019 606 © ASCE Enhanced Welding Operator Quality Performance Measurement: Work Experience- Integrated Bayesian Prior Determination Yitong Li, S.M.ASCE1; Wenying Ji, A.M.ASCE2; and Simaan M. AbouRizk, M.ASCE3 1Ph.D. Student, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030. E-mail:
[email protected] 2Assistant Professor, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030. E-mail:
[email protected] 3Professor, Dept. of Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB T6G 2W2, Canada. E-mail:
[email protected] ABSTRACT Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes. INTRODUCTION Pipe spool fabrication is essential to the successful delivery of an industrial construction project (Wang et al. 2009). During the process of pipe fabrication, welding is an important step and its quality must be examined to ensure the specified requirements are satisfied. Although welding is undertaken by skilled operators, variations commonly exist in welding operator quality performance due to the lack of essential knowledge and skills (Ji et al. 2018). Therefore, being able to reliably measure welding operator quality performance is crucial since the reliable performance measurement leads to considerable advancement in project quality performance, which would further decrease rework cost and overcome schedule delays. To achieve this goal, Ji and AbouRizk (2018) have developed a Bayesian statistics-based method to estimate operator quality performance by assuming operator quality performance is stationary over time. However, one of the most significant factors—the learning effect (i.e., the continuously improved quality performance as operator work experience increases)—was neglected, which leads to a biased estimation of operator quality performance. This research aims to enhance the previously developed approach to reliably measure welding operator quality performance by incorporating the effect of work experience. Specifically, the objective is achieved by (1) selecting a learning curve model to describe the relationship between quality performance and work experience; (2) applying the beta-binomial regression to derive the equation of the selected model; (3) determining a informative prior to Computing in Civil Engineering 2019 D ow nl oa de d fr om a sc el ib ra ry .o rg b y G eo rg e M as on U ni ve rs ity o n 03 /1 3/ 20 . C op yr ig ht A SC E . F or p er so na l u se o nl y; a ll ri gh ts r es er ve d. Computing in Civil Engineering 2019 607 © ASCE represent quality performance for a given operator; (4) demonstrating the advantages of the enhanced Bayesian-based approach using a case study. The remainder of this paper is arranged as follows. In the next section, previous work on Bayesian-based operator quality performance measurement is discussed. After that, the newly proposed methodology is introduced step by step. In the following section, a practical case study is conducted to demonstrate the advantages of the newly proposed approach. Finally, contributions, limitations, and future work are concluded. PREVIOUS WORK Previously, Ji and AbouRizk (2017) have advocated the advantages of using a Bayesian- based operator quality performance measurement to incorporate inspection sampling uncertainty. In their research, operator quality performance is reflected by fraction nonconforming (i.e., percentage repair rate) as indicated below: X p n (1) Where p denotes fraction nonconforming, X denotes the number of welds which fails inspections, and n denotes the total number of welds. To cover the sampling uncertainty, a beta distribution , Beta a b was chosen to model the prior distribution for the Bayesian-based fraction nonconforming estimation. The prior distribution represents operator quality performance when no inspection results are collected. The posterior distribution describes the latest measurement of operator quality performance by continuously adding more inspection results. An analytical solution for the posterior distribution follows: , Beta X a n X b (2) In Bayesian statistics, two types of priors are commonly used, namely, informative priors— probability distributions derived from historical data or subjective knowledge; and noninformative priors—vague, flat, and diffuse probability distributions that have the lowest bias to prior estimation when information is insufficient (Ji and AbouRizk 2017). In estimating the welding operator quality performance, Ji and AbouRizk (2017) used a noninformative prior distribution 1/ 2,1 / 2Beta without incorporating the learning effect of operators. The reliability of a Bayesian statistic-based method is heavily dependent on the prior determination (Winkler 1967). Incapable of determining reliable priors leads to unreliable posterior inferences, which further misleads practical decision support. Therefore, in aims of improving the existing approach, an informative prior determination method, which is able to incorporate work experience, is developed in this study. METHODOLOGY The research methodology of this study is demonstrated as Figure 1. First, the Plateau learning curve model is selected to illustrate the relationship between operator quality performance and work experience. Then, a beta-binomial regression approach is utilized to derive the unknown parameters for the selected learning curve model. After that, informative priors are determined through the derived learning curve equation. Lastly, posterior distributions are computed by incorporating newly collected inspection data. Computing in Civil Engineering 2019 D ow nl oa de d fr om a sc el ib ra ry .o rg b y G eo rg e M as on U ni ve rs ity o n 03 /1 3/ 20 . C op yr ig ht A SC E . F or p er so na l u se o nl y; a ll ri gh ts r es er ve d. Computing in Civil Engineering 2019 608 © ASCE Plateau Learning Curve Modeling Beta-Binomial Regression Informative Prior Modeling Posterior Distribution Determination Figure 1. Research methodology flow chart. Plateau Learning Curve Modeling: The Plateau model (Baloff 1971) describes a linear-log model with a constant term which indicates the operator’s steady-state performance. The Plateau model is selected to represent the relation between welding operator quality performance and work experience. It is applicable in this research because operator quality performance reaches a steady-state as operators gain enough practices. The Plateau model in this research is represented in Equation (3): A B( ) CFN n (3) Where FN denotes fraction nonconforming and n denotes the total number of welds. A, B, and C are unknown parameters that can be derived using the regression approach described in the next section. Beta-Binomial Regression: Regression is a statistical technique to determine the relationship between dependent variable and independent variables. For this research, the beta- binomial regression model is selected to derive parameters in the Plateau model. R's gamlss package (Stasinopoulos and Rigby 2018) is a regression package which allows all the parameters of the distribution of the dependent variable to be modeled as non-linear functions of the independent variables (Rigby and Stasinopoulos 2005). In this study, gamlss function is used to model the mean and the variation of fraction nonconforming (i.e., dependent variable) as a non-linear function of the total number of welds (i.e., independent variable). Here, variations of fraction nonconforming are assumed to be the same for all values of total welds and are represented as FN . The relationship between the mean value of fraction nonconforming and total number of welds follows Equation (3) can be represented as: A B( ) CFN n (4) This equation allows defining an exclusive mean value of fraction nonconforming for every operator based on their total number of welds (i.e., work experience). Informative Prior Modeling: In the Bayesian-based operator quality performance measurement approach, the prior distribution of fraction nonconforming is represented with a beta distribution as shown in Equation (5), which can be reparametrized using and , where (shown as Equation (6)) is the mean value of a beta distribution, and (shown as Equation (7)) represents the spread of the distribution. The reparametrized equation is shown as Equation (8). ,Beta a b (5) a a b (6) 1 a b (7) , ,1 , FN i FN i FN FN Beta (8) In Equation (8), ,FN i is computed from Equation (4) which represents the mean value of fraction nonconforming for operator i with the total number of welds in . The reparametrized Computing in Civil Engineering 2019 D ow nl oa de d fr om a sc el ib ra ry .o rg b y G eo rg e M as on U ni ve rs ity o n 03 /1 3/ 20 . C op yr ig ht A SC E . F or p er so na l u se o nl y; a ll ri gh ts r es er ve d. Computing in Civil Engineering 2019 609 © ASCE beta distribution is used as the informative prior distribution for the Bayesian-based approach