This paper presents a method and prototype implementation for tracking and assessing the structural condition of bridges. The method uses building information modeling (BIM) software to link and analyze data related to the inspection, evaluation, and management of bridges. In the proposed method, an inspector uses a custom software add-in, developed by the authors, to capture information on damage type, amount, severity, and location gathered during bridge inspection. Next, a custom tool in Microsoft Excel evaluates the structural performance and provides load ratings of the inspected bridge. The proposed method was validated through field testing on a case study bridge. Findings from this study suggest that BIM can effectively facilitate the inspection and evaluation of bridges, which may, in the future, enable transportation agencies to more efficiently manage bridge inventories and lead to a more automated practice.

Under the umbrella of a departmental initiative termed CM Cares, faculty from Colorado State University have developed a service-learning course to teach and implement concepts related to sustainability in construction education. The course has met with significant success and received recognition from community partners, elementary to graduate students, faculty members, and department and university administration. Beyond the positive response to experiential learning and community action, this paper uses data from the second course offering to assess the course’s effectiveness for teaching sustainability competencies. The writers implemented multiple techniques to collect data and assess perceived learning with regard to sustainability competencies. Techniques included surveys, reflection essays, and concept maps. Overall findings were mixed but suggest that significant learning about sustainability can occur using service-learning as a teaching technique in construction education despite (and p...

The authors, faculty members in the Department of Construction Management at Colorado State University, collaborated with industry to develop three interactive, pilot Building Information Modeling (BIM)-enabled educational modules designed to support and enhance spatial understanding, interoperability, and communication within construction education and training. The first module highlights masonry construction sequencing, the second highlights the cross-disciplinary aspects of structural analysis, and the third provides bilingual safety-training by integrating voice-narration and site specific 3D visualization. The initial development, implementation, and assessment of the three modules indicate that their use is beneficial to students and professionals, and that more research is merited. The paper previews the modules and presents preliminary lessons learned from their development and pilot implementations.

Over 6 million older Americans live with Alzheimer’s disease and related dementias; Black American older adults’ prevalence is more than twice that of non-Hispanic White older adults. The Black American dementia caregiving experience can be encapsulated within the Black Family Socioecological Context Model, which provides a conceptual basis for examining social determinants of health at individual, family, community, and societal levels with careful consideration for how the intersection of race, gender, and class of Black American dementia caregivers influences the multiple dimensions of their caregiving experiences. Family dynamics, community setting, and health care systems have a potentially bidirectional influence on these caregivers, which is shaped by historical and ongoing systemic and institutional racism and general disenfranchisement. This Forum article outlines how the Social Cognitive Theory offers ways for Black American dementia caregivers to achieve a sense of mastery within the complicated and fraught ecology within which their caregiving occurs. We propose a research agenda to create programs and interventions for enhancing a sense of mastery among Black American dementia caregivers. Two concepts in particular, “constraints” and “efficacy expectations,” provide ways to create a systematic approach to developing successful coping strategies for the constraints perceived by individuals as they undertake and function in the caregiving role. The recognition of the complexity of the caregiving ecosystem and intersectionality of caregivers’ experience and identity emphasize the importance of individualization: Each caregiver’s experience of this ecosystem—and therefore each Black American dementia caregiver’s way to mastery within it—will be uniquely shaped and experienced.

Thermal comfort of building occupants is a major criterion in evaluating the performance of building systems. It is also a dominant factor in designing and optimizing building’s operation. However, existing thermal comfort models, such as Finger’s model currently adopted by ASHRAE Standard 55, rely on factors that require bulky and expensive equipment to measure. This paper attempts to take a radically different approach towards measuring the thermal comfort of building occupants by leveraging the ever-increasing capacity and capability of mobile and wearable devices. Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture a number of important parameters that may be used to measure thermal comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. This research evaluates such opportunities by fusing traditional environmental sensing data streams with newly available wearable sensing information. Furthermore, it identifies challenges for using existing wearable devices and to developing new models to predict human thermal comfort. Findings from this exploratory study identify the inaccuracy of sensors in cellphones and wearable as a challenge, yet one which can be improved using customized wearables. The study also suggests there exists a high potential for developing new models to predict human thermal sensation using artificial neural networks and additional factors that can be individually, unobtrusively, and dynamically measured using wearables.