We consider the access problem in a multichannel opportunistic communication system with imperfect sensing, where the state of each channel evolves as a nonidentical and independently distributed Markov process. This problem can be cast into a restless multiarmed bandit (RMAB) problem, which is intractable for its exponential computation complexity. A promising approach that has attracted much research attention is the consideration of an easily myopic policy that maximizes the immediate reward by ignoring the impact of the current policy on future reward. Specially, we formalize a family of generic functions, which is referred to as g-regular functions, characterized by three axioms, and then establish a set of closed-form conditions for the optimality of the myopic policy and illustrate the engineering implications behind the obtained results.

Construction cost overrun chronically plagues contractors. To address the issue, numerous studies have explored cost overrun risks (CORs). Nevertheless, their methods of identifying risk relationship are susceptible to experts’ experience. In addition, they fail to unearth the relationship structure information and analyze the risk propagation effect. To fill these gaps, this paper intends to propose a methodology that integrates the engineering case analysis and complex network theory, so as to obtain a stable relationship structure and reveal its inherent property. First, 52 CORs and 158 risk paths are extracted from 156 engineering cases, followed by the establishment of a cost overrun risk propagation network (CORPN). Finally, the statistical properties of CORPN are explored. The results indicate that CORPN presents the topological property of heterogeneity. A large number of risk paths can be blocked through preventing the CORs with large total degree, like delay in construction period and engineering quantity increase. Meanwhile, CORPN shows the small-world property. The efficiency of risk propagation can be reduced through preventing the CORs with high betweenness centrality, such as lack of technical skill and experience. These findings contribute to the formulation of beforehand strategies that promote the cost management.

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

Dams are vital for water resource utilization, and river diversion is key for dam construction safety. As sandy river basins are important exploitation areas that have special diversion features, the impact of sediment on the risk of river diversion during dam construction should be assessed. Diversion uncertainty is the origin of diversion risk, and sediment uncertainty changes the storage and discharge patterns of the diversion system. Two Gumbel–Hougaard (GH) copula functions are adopted to couple the random variables of flood and sediment, so that the sediment impacts on diversion storage and discharge can be obtained by the sampling of flood peaks. Based on variable coupling and sediment amendment, a method of Monte Carlo simulation (MCS) with a water balance calculation can quantitatively assess the risk of sandy river diversion, by evaluating the probability of upstream cofferdam overtopping. By introducing one diversion project on the Jing River in China with a clear water contrast, the risk values of dam construction diversion with or without sediment impacts can be obtained. Results show that the MCS method is feasible for diversion risk assessment; sediment has a negative impact on the risk of river diversion during dam construction, and this degradation effect is more evident for high-assurance diversion schemes.