A Generalized Methodology to Characterize Composite Materials for Pyrolysis Models

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.

Large-Scale Controlled-Condition Experiment to Evaluate Light Weight Deflectometers for Modulus Determination and Compaction Quality Assurance of Unbound Pavement Materials

Compaction control using lightweight deflectometers (LWD) is currently being evaluated in several states and countries and fully implemented for pavement construction quality assurance (QA) by a few. Broader implementation has been hampered by the lack of a widely recognized standard for interpreting the load and deflection data obtained during construction QA testing. More specifically, reliable and practical procedures are required for relating these measurements to the fundamental material property—modulus—used in pavement design. This study presents a unique set of data and analyses for three different LWDs on a large-scale controlled-condition experiment. Three 4.5x4.5 m2 test pits were designed and constructed at target moisture and density conditions simulating acceptable and unacceptable construction quality. LWD testing was performed on the constructed layers along with static plate loading testing, conventional nuclear gauge moisture-density testing, and non-nuclear gravimetric and volumetric water content measurements. Additional material was collected for routine and exploratory tests in the laboratory. These included grain size distributions, soil classification, moisture-density relations, resilient modulus testing at optimum and field conditions, and an advanced experiment of LWD testing on top of the Proctor compaction mold. This unique large-scale controlled-condition experiment provides an excellent high quality resource of data that can be used by future researchers to find a rigorous, theoretically sound, and straightforward technique for standardizing LWD determination of modulus and construction QA for unbound pavement materials.

ENGINEERING DIGITAL SHARING PLATFORM TO CREATE SOCIAL CONTAGION: EVIDENCE FROM LARGE SCALE RANDOMIZED FIELD EXPERIMENTS

Peer-to-peer information sharing has fundamentally changed customer decision-making process. Recent developments in information technologies have enabled digital sharing platforms to influence various granular aspects of the information sharing process. Despite the growing importance of digital information sharing, little research has examined the optimal design choices for a platform seeking to maximize returns from information sharing. My dissertation seeks to fill this gap. Specifically, I study novel interventions that can be implemented by the platform at different stages of the information sharing. In collaboration with a leading for-profit platform and a non-profit platform, I conduct three large-scale field experiments to causally identify the impact of these interventions on customers’ sharing behaviors as well as the sharing outcomes. The first essay examines whether and how a firm can enhance social contagion by simply varying the message shared by customers with their friends. Using a large randomized field experiment, I find that i) adding only information about the sender’s purchase status increases the likelihood of recipients’ purchase; ii) adding only information about referral reward increases recipients’ follow-up referrals; and iii) adding information about both the sender’s purchase as well as the referral rewards increases neither the likelihood of purchase nor follow-up referrals. I then discuss the underlying mechanisms. The second essay studies whether and how a firm can design unconditional incentive to engage customers who already reveal willingness to share. I conduct a field experiment to examine the impact of incentive design on sender’s purchase as well as further referral behavior. I find evidence that incentive structure has a significant, but interestingly opposing, impact on both outcomes. The results also provide insights about senders’ motives in sharing. The third essay examines whether and how a non-profit platform can use mobile messaging to leverage recipients’ social ties to encourage blood donation. I design a large field experiment to causally identify the impact of different types of information and incentives on donor’s self-donation and group donation behavior. My results show that non-profits can stimulate group effect and increase blood donation, but only with group reward. Such group reward works by motivating a different donor population. In summary, the findings from the three studies will offer valuable insights for platforms and social enterprises on how to engineer digital platforms to create social contagion. The rich data from randomized experiments and complementary sources (archive and survey) also allows me to test the underlying mechanism at work. In this way, my dissertation provides both managerial implication and theoretical contribution to the phenomenon of peer-to-peer information sharing.