Predicting early-age thermal behavior of mass concrete for bridge foudnation

Large volume concrete continues to present increasing concern in the construction of bridges. Due to the increased heat generation during massive concrete foundation construction, the concern for cracking, resulting from thermal contraction, is increased. To reduce the thermal cracking potential and ensure the safety and durability of the foundation structure, several practices are utilized, such as using low heat generating cement, proper insulation, cooling pipes and so forth. The objectives of this research are to explore the current available early age mass concrete thermal analysis software packages and provide recommendations for mass concrete construction practices. Currently, there are several computer software packages capable of evaluating the early age development of concrete. 4C Temp&Stress, which is discussed in detail in this study, is a user friendly and flexible software package capable of analyzing mass concrete structures. The sensitivity study results of using 4C Temp&Stress indicate that a reduced least dimension, extended for removal time, and reduced fresh placement temperature could reduce the maximum temperature and decrease cracking potential. Current insulation and formwork materials and practices are confirmed to be to a practical approach in mass concrete construction. The recommended layout and dimensions of post cooling systems will be discussed to provide the most efficiency cooling system. Besides these, other methods and strategies are investigated in case study for controlling the thermal cracking of massive concrete placements. The findings of this study also indicate relationship between maximum temperature difference and maximum tensile stress/strength ratio at different time interval, and between the concrete maximum temperature and maximum temperature difference with the fresh placement temperature of the concrete and the depth of concrete for a specific mix design in mass concrete projects. The models allow contractors, or the Iowa Department of Transportation (Iowa DOT) to roughly estimate the thermal behavior and cracking potential

Modelling the Passenger Choice Behaviour of Buying High-Speed Railway Tickets

Passenger choice behaviour of buying tickets has a great impact on the high-speed rail (HSR) revenue management. It is very critical to find out the sensitive factors that prevent passengers with high willingness to pay for a ticket from buying low-price tickets. The literature on passenger choice behaviour mainly focuses on travel mode choice, choice between a conventional train and a high-speed train and choice among high-speed trains. To extend the literature and serve revenue management, this paper investigates passenger choice behaviour of buying high-speed railway tickets. The data were collected by the stated preference (SP) survey based on Beijing-Hohhot high-speed railway. The conditional logit model was established to analyse influencing factors for business travel and non-business travel. The results show that: business passengers have the higher inherent preference for full-price tickets, while non-business passengers have the higher inherent preference for discount tickets; the number of days booked in advance and frequent passenger points have a significant impact on the ticket choice of business travellers, but not on non-business travellers; passengers are unwilling to buy tickets that depart after 16:00 for non-business travel; factors have different effects on the passengers\u27 choice in business travel and non-business travel. The results can provide parameters for revenue management models and references for the ticket-product design

CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting extrinsic rewards for each transition in the offline data. As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards. To achieve that, we introduce \textbf{C}alibrated \textbf{L}atent g\textbf{U}idanc\textbf{E} (CLUE), which utilizes a conditional variational auto-encoder to learn a latent space such that intrinsic rewards can be directly qualified over the latent space. CLUE's key idea is to align the intrinsic rewards consistent with the expert intention via enforcing the embeddings of expert data to a calibrated contextual representation. We instantiate the expert-driven intrinsic rewards in sparse-reward offline RL tasks, offline imitation learning (IL) tasks, and unsupervised offline RL tasks. Empirically, we find that CLUE can effectively improve the sparse-reward offline RL performance, outperform the state-of-the-art offline IL baselines, and discover diverse skills from static reward-free offline data

Beyond Reward: Offline Preference-guided Policy Optimization

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023