Search

This paper presents the development of a comprehensive campus facility management performance assessment model (CFMPM). The model employs structural equation modeling (SEM) and incorporates a thorough list of crucial success factors. The CFMPM tool is intended for use by industry shareholders to evaluate the operational effectiveness of campus facility management (CFM) and analyze the proficiency and constraints of CFM organization across diverse managerial tiers. Key CFM success factors were collected using interviews with experts and relevant literature, validated using two rounds of the Delphi technique with 14 highly qualified experts, and disseminated via an online questionnaire to industry experts who rated them according to their significance. Data were obtained from 402 participants hailing from diverse regions. Then, SEM was used to perform a quantitative analysis of the interconnectedness between the various aspects of CFM performance, with the aim of ascertaining the influence of each construct on overall performance. The model was rigorously assessed for its robustness across several parameters, including goodness-of-fit measurements, multivariate normality tests, and reliability and validity tests, in addition to hypothesis analysis. The gathered data were used to construct a CFMPM comprising 45 key performance indicators of successful CFM grouped into seven different constructs. The application of SEM revealed a noteworthy correlation between the operational indicators of proficient CFM and the construct groups, in addition to the impact of efficacious CFM constructs on CFM performance overall. This research enhances the existing knowledge base by identifying vital indicators for the evaluation of CFM effectiveness and utilizing them to formulate an inclusive global SEM capable of serving as a tool for gauging the efficiency of CFM. This could lend valuable support for organizations, facility managers, and policy makers when making well-informed decisions. [ABSTRACT FROM AUTHOR]

The construction industry has grown significantly to fulfill the expanding need for accommodation, facilities, welfare, and services resulting from the rapid increase in the global population. Infrastructure projects in particular facilitate transportation and services for both new and existing areas, yet they are also one of the primary producers of construction waste, which is harmful to both society and the environment. Hence, the objective of this paper is to identify the factors affecting construction waste management in infrastructure projects (CWMIIP) and study their respective impacts on construction waste management (CWM) performance. This study makes a unique contribution by not only addressing the factors that influence the development of construction wastes and their importance, but also by giving quantifiable solutions and action plans to reduce the waste. Using a literature review, 26 factors were selected as the major contributors to construction waste in infrastructure projects. An online questionnaire was then developed to collect data on the respective impacts of these factors. A total of 167 complete responses to the questionnaire were collected, and a structural equation model (SEM) was developed to quantitatively measure the impact of each factor on waste management performance. The model was validated using goodness of fit (GOF), multivariate normality, construct validity, reliability, and hypotheses analyses. The results indicate that the most significant factors affecting the generation of construction wastes are "quantity take-off error by contractor," "unforeseen incidents damaging site and/or completed works," "design errors," and "extreme weather conditions damaging completed works." The model results showed that all predefined hypotheses were supported, except for the positive effect of the logistics group, which was dropped during model development. Individual factors were also divided into groups, and the total impact of each group was assessed; the "management" group had the greatest impact, followed by "execution," "others," and "procurement." Based on these findings, we make several recommendations to construction industry professionals to reduce waste and mitigate its harmful effects, such as requiring a construction waste management implementation plan before awarding a project to a contractor; engaging with project suppliers to provide execution training for contractor staff; ensuring proper implementation of health, safety, and environmental plans; and purchasing locally available materials. This research built a model based on structural equation modeling that will help researchers better understand the links between building waste management parameters. To examine the impact of each waste component on construction waste management performance, a quantitative model is built based on the data obtained. This quantitative waste management model could be used to track construction waste management performance. The findings of this study are not limited to infrastructure projects because they may be tailored to fit any project. As a result, it is recommended that project contractors and supervision consultants rely on the factors responsible for construction waste generation identified in this study to capture their actual construction waste factors. Also, having a record and log of all construction wastes generated during a project, as well as the reasons for these wastes, would help the organization improve its waste management learning curve for future projects. [ABSTRACT FROM AUTHOR]

Promoting the safety of commercial trucks by identifying countermeasures that eliminate/reduce the effect of factors that increase the severity of truck-related crashes is crucial. Crash causal factors for rural interstate roads, located within the mountain plains, are inherently unique compared to urban interstate roads. This is due to the presence of challenging road geometry coupled with severe weather conditions and high truck traffic volumes. This study investigated Interstate 80 in Wyoming using decision trees, as a data mining approach, and structural equation model (SEM) as a latent factor modeling approach. SEM was employed to clarify the direct and indirect relationships between endogenous and exogenous variables while accounting for the variation and covariation within and between the constructed measurement models. Crash severity data were processed to account for factors affecting single vehicles and multivehicle trucks. The results showed that the interaction with surrounding traffic was the most significant latent variable affecting the crash severity of multivehicle truck crashes, while adverse weather conditions were the most significant latent variable affecting the crash severity of single-truck crashes. The results of this study highlighted the importance of increasing the situational awareness of commercial truck drivers with upcoming hazardous events. This could be performed by communicating information using variable message signs, the 511 application, the commercial vehicle operator portal (CVOP), or the connected vehicle (CV) technologies. [ABSTRACT FROM AUTHOR]

The adoption of human–robot collaboration (HRC) in various forms is widely expected to help improve productivity, reduce human physical workload, and alleviate the issues created by a skilled labor shortage in the construction industry. One potential deterrent to adoption of such collaborative work methods often cited by industry stakeholders is resistance by workers stemming from their fear of losing their jobs or having to learn new approaches and methods of performing construction work. Although significant prior studies have used path analyses to statistically evaluate hypotheses about technology adoption models by estimating the relationships in the models, no research has specifically investigated the intention to work in HRC through such an approach. This study addresses this knowledge gap and empirically investigates the factors affecting construction personnel's behavioral intention to accept HRC. HRC in this study is envisioned as robotic assistants performing the physically demanding repetitive work while construction personnel focus on cognitive tasks necessary to plan and supervise the work. An HRC adoption model based on the technology acceptance model (TAM) and innovation diffusion theory (IDT) is presented. This study expands a TAM-IDT model with new constructs such as job satisfaction and openness to training to explore an improved model that increases the understanding of construction personnel's intention to adopt HRC. Data collected from 156 construction personnel were analyzed to identify the key components based on exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The proposed model, consisting of eight external and four internal variables, was examined using structural equation modeling (SEM). Results indicated that variables such as perceived usefulness (PU) and perceived ease of use (PEU) had a positive and significant impact on behavioral intention (BI) to work in the described HRC system. Both theoretical and management implications emerging from the study are offered as insights to assist the construction industry in managing the introduction and adoption of HRC for on-site construction. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) adoption in construction remains a challenge, yet no empirical studies were conducted to gauge adoption behavior changes over time. Such a longitudinal study of BIM adoption would be particularly significant at this pivotal point in the history of construction, as it undergoes a worldwide digital transformation. In some contexts, BIM adoption has been led by contractors without policy intervention; thus, contractors' adoption behaviors must be understood before policymakers enact top-down interventions for public construction in entire supply chains. Two cross-sectional data sets were collected in 2017 (N=303) and 2020 (N=171) in Lima, Peru, to evaluate changes in a modified technology adoption model (TAM) regarding BIM adoption. Confirmatory factor analysis (CFA), structural equation modeling (SEM), and ANOVA were used to analyze the data. This study shows that contractors adopt BIM if they believe that BIM helps to improve project performance, rather than their own individual performance. Similarly, contractors are concerned with the easiness of implementing BIM in firms and projects rather than the ease of learning BIM. Overall, perceived usefulness to project, perceived ease of implementation, and intention to use BIM had a significant change between 2017 and 2020. Although BIM implementation increased from 25% to 39% at the project level, the adoption at the user level has not significantly changed. This finding suggests that contractors value supply chain/project level implementation benefits rather than successful individual adoption. The results also show that contractors rely on in-house BIM teams or outsourced consultants to carry out BIM-related tasks. However, this would depict a within-firm digital divide between BIM teams and site management. The results are discussed in the context of national change as a BIM policy for public construction was announced by the Peruvian government in 2019. The findings of this research may be used by decision makers to target specific groups of professionals on educational programs. Policymakers could use the findings to inform roadmaps for BIM adoption in Peru and similar countries. [ABSTRACT FROM AUTHOR]