Previous research in other disciplines identified that job satisfaction plays a major role in employee performance and retention. This paper examines the relationship between job satisfaction and quality of work-life (QWL) factors from the perspective of construction craft workers. The study analyzed data collected from 2002 to 2014 using the General Social Survey’s (GSS) QWL questionnaire module. Thirty-four of the 78 QWL variables were found to have a significant correlation with the overall level of job satisfaction of construction craft workers. By using the exploratory factor analysis, five latent factors—safety priority and organizational effectiveness, fair rewards system, resource adequacy, physical and mental health, and job tenure—were extracted. The relative impact of the five latent factors on construction craft worker job satisfaction also was identified. The paper contributes to the overall body of knowledge by identifying latent factors of QWL that affect craft worker job satisfacti...

With a significant number of steel bridges approaching the end of their service life, understanding deterioration characteristics will help bridge stakeholders better prioritize bridge maintenance, repairs, and rehabilitation as well as help with budget planning. This paper applies data mining techniques including logistic regression, decision trees, neural networks, gradient boosting, and support vector machine to the United States’ national bridge inventory to estimate the probability of steel bridge superstructures reaching deficiency. A focused subset of data was created based on the defined scope of the research: design material (steel), type of design (stringer/multibeam or girder), and deck type (cast-in-place concrete). The predictors of the model include age, average daily traffic, design load, maximum span length, owner, location, and structure length. The magnitude that these factors contribute to the likelihood of a steel bridge superstructure’s deficiency was identified. Outcomes of the analysis afford bridge stakeholders the opportunity to better understand the factors that are correlated to steel bridge deterioration as well as provide a means to assess risks of superstructure deficiency for the sake of prioritizing bridge maintenance, repair, and rehabilitation.

State transportation agencies (STAs) have been using the project bundling method primarily on bridge projects. While many studies suggest that bundling bridges can reduce project costs and expedite project delivery, more research is needed to help STAs to achieve advanced use of project bundling by understanding the current state of the practice and perceptions about project bundling among STA representatives. This paper presents the detailed findings of a survey, including the perceived benefits, challenges, and risks involved in the bundling method among STA representatives. The survey found that project bundling helps reduce individual project costs in a bundle as well as agency administrative costs and provides an individual project with the flexibility to schedule around other projects in a bundle. Project bundling was found to have a major impact on the cost and delivery time of the project. The major factors that are considered for bundling effectiveness were found to be work type, project type, and spatial proximity. The major concern that most of the STAs had during bundling was that the bundled projects may eliminate the competition from smaller contractors. However, the concerns over the bundling method were found to be less among agencies with a high-level experience of bundling. The survey also found that the majority of STAs have not quantified the impact of the project bundling yet. The results from this study suggest that further research on bundling mixed-type projects and risks associated with bundled contracts is necessary.

Steel construction activities are often undertaken in an environment with limited climate control. Both hot and cold temperatures can physically and psychologically affect construction workers, thus decreasing their productivity. Temperature and humidity are two factors that constantly exert forces on workers and influence their performance and efficiency. Previous studies have established a relationship between labor productivity and temperature and humidity. This research is built on the existing body of knowledge and develops a framework of integrating building information modeling (BIM) with a lower level critical path method (CPM) schedule to simulate the overall impact of temperature and humidity on a healthcare facility’s structural steel installation project in terms of total man hours required to build the project. This research effort utilized historical weather data of four cities across the U.S., with each city having workable seasons year-round and conducted a baseline assessment to test if various project starting dates and locations could significantly impact the project’s schedule performance. It was found that both varied project start dates and locations can significantly contribute to the difference in the man hours required to build the model project and that the project start date and location can have an interaction effect. This study contributes to the overall body of knowledge by providing a framework that can help practitioners better understand the overall impact of a productivity influencing factor at a project level, in order to facilitate better decision making.

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.

Fire safety on building construction sites as an important part of on-site safety management has rarely been studied for construction sites. To fill the knowledge gap, this research was conducted to identify important influencing factors on fire safety on construction sites, as well as their relationships, through a questionnaire administered to 1,492 fire officers in South Korea. The authors divided the questions into three factors (building, fire, and human) to distinguish factors affecting construction site fire safety. For the majority of the factors, there was high agreement on the importance level of factors affecting fire safety during construction between administrative and field firefighters. However, perception disparity did exist. The authors identified the interrelationships between factors through path analysis and structural equation modeling. The paths of factors showed the highest direct relation between building and fire factors, and the human factor played a mediator role between human and fire factors. The study contributes to the overall body of knowledge by providing officers’ insights into the factors contributing to fire safety on construction sites, as well as the quantification of the interrelationship among the factors, which will serve as the basis to develop effective safety measures, such as policies and regulations at both the project level and industry level, to address fire safety on construction sites. The SEM model developed in this study can also be used as a base model for future related studies.