Evidence combination rule with contrary support in the evidential reasoning approach

Three aspects of problems such as reasonable weight constraint, cumulative weight effect and relative weight equivalence cannot be well reflected in the evidential reasoning (ER) approach. In order to solve the above problems, a contrary support is defined to restrict the degree influenced by the evidence to be combined in combination process. Then pair-weighted and cumulative pair-weighted discounting methods are presented to generate basic probability assignment (BPA) for evidence. Pair-wised and recursive combination rules are established to make combination with the BPA of evidence by orthogonal sum operation and several theorems such as relative weight equivalence are proved. A combination algorithm is proposed to solve multiple criteria decision making (MCDM) problems by integrating pignistic probability and expected utility with the established combination rules. Finally, an illustrative example is provided to demonstrate the applicability of the proposed combination rules and algorithm.     

Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Slow-moving or stopped trains at highway-railroad grade crossings, especially in the populated metropolitan areas, not only cause significant traffic delays to commuters, but also prevent first responders from timely responding to emergencies. In this study, the researchers introduce an automated video analysis, detection and tracking system to evaluate the traffic conditions, analyze blocked vehicle behaviors at grade crossings, and predict the decongestion time under a simplified scenario. A novel YOLOv3-SPP+ model has been developed to improve the detection performance with dividing the image from finer to coarser levels and enhance local features. The SORT module has been integrated to the model for a simple yet efficient manner to track vehicles at the railroad grade crossing. Two field datasets at the Columbia, SC, with train blockage video records have been tested. The model training performance has been evaluated by mAP @0.5, F1 score, and total loss. Based on the training results, our model outperforms other YOLO series models. The field tracking performance has been assessed by the ratio between prediction and ground truth. The mean value of accuracy of our test cases is 92.37%, indicating a reliable tracking performance. In addition, the present results indicate the traffic during and after the crossing blockage does follow a pattern, and there is a general trend of the behavior of the vehicles waiting or taking an alternative route. A good linear correlation between the decongestion time and the number of blocked vehicles has been observed at the monitored grade crossing at the City of Columbia, SC.     

Comparing paradigms for strategy learning of route choice with traffic information under uncertainty

This paper aims to model the traveller's day-to-day route choice in the case of an Advanced Traveller Information System (ATIS) through two learning paradigms: reinforcement-based and belief-based. The reinforcement learning approach is adopted in both a basic version and an extended one. Similarly, the belief-learning approach is adopted in both a Joint Strategy Fictitious Play version and in a Bayesian-learning one. All the models are compared and validated based on data collected by means of a stated preference experiment. The models explicitly account for the reliability of the information system, as this interacts with the inherent dispersion of network travel times and determines the overall level of uncertainty affecting the travellers’ adaptive learning behaviour. The experiment is then designed to simulate different levels of reliability for the ATIS. Results show that for intermediate and high levels of information accuracy, joint strategy fictitious play best predicts the respondents’ route choice behaviour under information provision, suggesting that a best-reply strategy is used by travellers for their route choices. In low information accuracy, the result suggests the payoff variability moves the choice behaviour toward randomness. The proposed approach provides useful tools to model travellers’ adaptive route choice behaviour and contributes to the support of effective ATIS design.     

Optimal periodic scheduling of multipurpose plants

This paper considers the problem of deriving an optimal periodic schedule for general multipurpose plants comprising batch, semi-batch and continuous operations. This involves the determination both of the optimal duration of the operating cycle, and of the precise nature and timing of operations taking place within it. The basis of our formulation is the general resource-task network (RTN) process representation coupled with a continuous representation of time. The presented mathematical formulation results in a mixed integer non-linear programming (MINLP) problem which cannot be linearized exactly. We describe a special branch-and-bound algorithm for solving this problem by branching on both discrete and continuous variables. The overall approach can be parallelized in an effective manner as demonstrated by the examples presented.     

Detection of filamentous and nitrifying bacteria in activated sludge with 16S rRNA probes

Activated sludge samples from wastewater treatment plants from potato starch and starch derivatives factory and from a municipal sewage treatment plant were analyzed with DNA probes specific for several filamentous bacteria. It was found that Haliscomenobacter hydrossis, Sphaerotilus natans, Thiothrix sp. and Eikelboom Type 021N were common in the activated sludges. Fluorescent in situ Hybridization (FISH) analysis could detect more types of sheathed bacteria and yielded a more accurate quantification of bacteria than conventional microscopy.In a pilot and a full scale wastewater treatment plant (WWTP) clear correlations were found between the SVI and growth of a Sphaerotilus natans and a Thiothrix sp. Addition of chlorine to the bulking sludge resulted in an improved SVI of the sludge but only damaged filamentous cells outside the floc. Nitrification was measured with substrate depletion and FISH analysis. Signal interpretation of FISH analysis was demonstrated both manually and with automated image analysis.     

Damage detection using self-powered wireless sensor data: An evolutionary approach

A major limitation in implementation of self-powered wireless sensors pertains to considerable loss of the sensed information. Consequently, interpretation of the limited but valued data generated by the self-powered wireless sensing technology becomes a challenging problem. To tackle this issue, this study presents an evolutionary computational approach for structural damage detection using the self-powered wireless sensor data. The proposed data interpretation system is based on the integration of a robust evolutionary technique, called gene expression programming (GEP), and finite element (FE) method. Several damage indicator variables are extracted upon the simulation of the compressed data stored in memory chips of self-powered sensors. For the analysis, the complicated case of gusset plate of bridge is considered. Different damage scenarios are introduced to the plate and for each scenario and sensor configuration, a damage detection model is derived. Bases on a logistic regression analysis, probabilities are assigned to each model to find the most probable damage state. The damage detection models are presented as MATLAB and Visual Basic codes for further analysis. An uncertainty analysis is performed through the contamination of the damage indicator features with different noise levels. The results indicate that the proposed method is efficiently capable of detecting different damage states in spite of high-level noise contamination.     

Mechanism and control of the long-term performance evolution of structures

It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms. The elucidation of the degradation mechanisms and the realization of effective long-term performance degradation control have been a research frontier in the field of civil engineering in recent years. Currently, the major topics that concern this research frontier include revealing the physical and chemical mechanisms of structural performance evolution under long-term environmental exposure and loading and developing structural performance degradation control technologies based on fiber-reinforced materials, for example, fiber-reinforced polymers (FRPs) and fabric-reinforced cementitious matrix (FRCM). In addition, there are novel structural performance control technologies, such as using a shape memory alloy (SMA) and self-healing concrete. This paper presents a brief state-of-the-art review of this topic, and it is expected to provide a reference for subsequent research.     

The effects of joints on rock fragmentation by TBM cutters using General Particle Dynamics

In this paper, a novel meshfree numerical method known as General Particle Dynamics (GPD) is proposed to reveal the mechanism of the rock fragmentation by TBM cutters. Rock fragmentation by two cutters in consecutive joints rock is investigated using GPD. The numerical results obtained from GPD are in good agreement with the field observed results. Moreover, the effects of the length of intermittent joints on rock fragmentation by two cutters are investigated using GPD. It is found from the numerical results that the length of intermittent joints can significantly influence the crack initiation and propagation and coalescence as well as the fragmentation pattern, and hence affect the penetration rate of the TBM. It indicates that GPD has the potential in simulating rock indentation and fragmentation by TBM cutters.