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San Luis Obispo High School FFA Farm Chicken Coop Construction
Get PDFThis paper covers the construction process by a team of students from California Polytechnic State University, San Luis Obispo (Cal Poly) to provide a chicken coop for the San Luis Obispo High School FFA Farm. First initiated in Fall 2023, the project will replace the existing temporary chicken coops with a larger and more functional structure to house over 10 chickens. This paper focuses on the construction phase, but also briefly touches on the design and pre-construction phases. Through meetings with the client, the team of students developed a design that met the high school’s criteria and needs. Key features included a raised, fully enclosed coop with nesting boxes and student access. Feedback from the school and Cal Poly advisor, Joe Cleary, led to adjustments such as repositioning the entrance, enlarging the nesting boxes, incorporating a perch, redoing the foundation, and reworking the roof. The paper concludes with value engineering, results, lessons learned, detailed phasing, and a builder-focused mindset in construction projects. After a long quarter, the team is satisfied with the construction outcome achieved during the final weeks of Spring 2024
Construction Software: Use, Selection, and Performance in Low-Bid Contracting
Get PDFThe qualitative research included in this study evaluates the use, selection, and perceived gaps of current construction software suits in low-bid contracting. In addition, the data aims to provide merit to the correlation between construction software use and company performance. The intended stakeholders of this study include entrepreneurs in software development and construction, along with researchers looking to gather knowledge pertaining to this niche industry. Through a Google Forms questionnaire distributed via email, participants provide insight on the current status quo. The study’s responses show clear trends in the types of software currently in use and the subsequent reasons for selection. The software categories analyzed included takeoffs, scheduling, estimating, and document management. Concluding data reveals that the most popular software platforms are Bluebeam, Primavera 6, Heavy Bid, and cloud-based folders for their respective categories. Moreover, the most desirable software traits are found to be advanced functionality, user friendliness, and familiarity. While the perceived gaps in current technology include an equal split between user friendliness, full-service offerings, and no gaps. This data provides insights into the software preferences and practices of low-bid contractors. It also confirmed that there is a correlation between the amount of software used and company performance
Supersonic Air Inlet Modeling Using the Method of Characteristics
Get PDFThe Air Inlet Method of Characteristics Analysis Tool (AIMCAT), a tool based in Python 3, is developed to model supersonic air inlet geometries during the early phases of design. The method of characteristics (MOC) is used to solve the governing equations for an inviscid, irrotational, isentropic, steady, supersonic flowfield. A comparison is made between modeling shock waves implicitly using Mach wave coalescence and modeling them explicitly using oblique shock relations. Multiple test cases are used to assess the accuracy of the tool by comparing to experimental wind tunnel data. Good general agreement was achieved over the majority of the supersonic portion of the flowfield for all test cases. The implicit shock mesh achieved better accuracy for shock wave positions compared to the explicit shock mesh. However, the explicit shock mesh captured total pressure losses across the shocks which is of value when assessing the efficiency of the inlet. Both approaches show their respective values and their suitability depends on the conditions being studied. AIMCAT has shown initial promise, however further development is need to improve its utility and robustness
Development of Electrical Impedance Tomography Data Acquisition System and Deep Learning-Based Reconstruction Algorithms for Spatial Damage Detection
No full textElectrical impedance tomography (EIT) is a non-destructive, non-invasive, and non-radioactive imaging technique used for reconstructing the internal conductivity distribution of a sensing domain. Performing EIT often requires large, stationary benchtop equipment that can be expensive and impractical. Other researchers have attempted to make portable EIT systems, but they all rely on external computation for image reconstruction/data analysis. This study outlines the development of a low-cost, portable, and wireless EIT data acquisition (DAQ) system that is capable of independently performing image reconstructions on-board. With the proposed system, EIT can be performed on carbon fiber reinforced polymers to spatially locate damages. Since EIT reconstruction algorithms can be extremely computationally intensive, this study has also developed an alternative deep-learning algorithm that leverages the compressed-sensing technique to strategically train a neural network. The proposed neural network has not only achieved comparable results to traditional iterative algorithms, but it can do so in a fraction of the time