A Bayesian-Based Approach for Public Sentiment Modeling

Public sentiment is a direct public-centric indicator for the success of effective action planning. Despite its importance, systematic modeling of public sentiment remains untapped in previous studies. This research aims to develop a Bayesian-based approach for quantitative public sentiment modeling, which is capable of incorporating uncertainty and guiding the selection of public sentiment measures. This study comprises three steps: (1) quantifying prior sentiment information and new sentiment observations with Dirichlet distribution and multinomial distribution respectively; (2) deriving the posterior distribution of sentiment probabilities through incorporating the Dirichlet distribution and multinomial distribution via Bayesian inference; and (3) measuring public sentiment through aggregating sampled sets of sentiment probabilities with an application-based measure. A case study on Hurricane Harvey is provided to demonstrate the feasibility and applicability of the proposed approach. The developed approach also has the potential to be generalized to model various types of probability-based measures

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

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.Comment: 8 pages, 5 figures, 2 tables, i3CE 201

Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks

This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multisource data generated from construction operations, which provides rich information for input modeling. The framework implements Bayesian deep neural networks to facilitate the purpose of incorporating richer information in input modeling. A case study on road paving operation is performed to test the feasibility and applicability of the proposed framework. Overall, this research enhances input modeling by deriving detailed input models, thereby, augmenting the decision-making processes in construction operations. This research also sheds lights on prompting data-driven simulation through incorporating machine learning techniques

Automated Integration of Infrastructure Component Status for Real-Time Restoration Progress Control: Case Study of Highway System in Hurricane Harvey

Following extreme events, efficient restoration of infrastructure systems is critical to sustaining community lifelines. During the process, effective monitoring and control of the infrastructure restoration progress is critical. This research proposes a systematic approach that automatically integrates component-level restoration status to achieve real-time forecasting of overall infrastructure restoration progress. In this research, the approach is mainly designed for transportation infrastructure restoration following Hurricane Harvey. In detail, the component-level restoration status is linked to the restoration progress forecasting through network modeling and earned value method. Once the new component restoration status is collected, the information is automatically integrated to update the overall restoration progress forecasting. Academically, an approach is proposed to automatically transform the component-level restoration information to overall restoration progress. In practice, the approach expects to ease the communication and coordination efforts between emergency managers, thereby facilitating timely identification and resolution of issues for rapid infrastructure restoration

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

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.Comment: 8 pages, 5 figures, 2 tables, i3CE 201