Purpose: The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).Design/methodology/approach: This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.Findings: To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.Practical implications: A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.Social implications: The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.Originality/value: In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

Purpose: The negative effects of job stress and burnout on construction professionals (CPs) at the construction site have been widely concern in the construction industry. The purpose of this study is committed to explore the impact of job stress on CPs on the construction site, especially in the context of the widespread use of social media to express their emotions.Design/methodology/approach: This study developed a job-related stress-burnout-health conditions-turnover intention (S-B-HT) framework to explore the direct and lagged effects of job stress, we also examined the moderating effects of online emotions, operationalized in terms of emotional intensity and expression pattern, on the relationship between job stress with job burnout under two evolution paths (i.e. health conditions or turnover intention). This study collected 271 samples through a survey questionnaire for empirical testing, and introduced structural equation models to validate the proposed conceptual model.Findings: The results show that job stress has a significant positive effect on job burnout, and job burnout maintains a positive relationship with health conditions (or turnover intention) under the interference mechanism. Simultaneously, the online emotions expressed in social media have a positive moderating effect in two stages of the evolution path.Practical implications: The findings of this study remind the project manager need to timely find and solve the job burnout characteristics of CPs due to excessive job stress, especially to prevent the accidental consequences caused by job burnout.Originality/value: On this basis, this study provides an important value of using social media to express emotions for the project team to alleviate the adverse of professionals under job stress.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors. This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction. The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms. Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

Purpose: The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.Design/methodology/approach: This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.Findings: The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.Practical implications: The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.Originality/value: The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information characteristics and uncertainty of knowledge migration, however, lead to the failure to transform most of the requirement information into effective knowledge. This study proposes a multi-stage knowledge transformation framework for realizing the transformation of SRs from abstract information to canonical knowledge, and it accurately completes the knowledge transformation through document matching, knowledge extraction, and knowledge representation. Meanwhile, a semi-automated model was introduced into this study to develop a domain ontology knowledge base for SRs and to represent each type of knowledge through class definitions. The proposed framework was validated by testing project documents collected from two types of building projects, and the results show that the RD-based association rules can accurately match documents associated with SRs and adapt to match different types of sentiment attribute documents. Moreover, the improved TF-IDF algorithm improved by 20% in precision and recall, showing that the algorithm can extract tacit knowledge by combining knowledge points. Further, the domain ontology knowledge base facilitates normative documentation and representation for each type of knowledge in SRs.