Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle's speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.
Biofuels from microalgae have promising potential for a sustainable bioeconomy. Algal strains' oil content and biomass yield are the most influential cost drivers in the fourth generation biofuel (FGB) production. Genetic modification is the key to improving oil accumulation and biomass yield, consequently developing the bioeconomy. This paper discusses current practices, new insights, and emerging trends in genetic modification and their bioeconomic impact on FGB production. It was demonstrated that enhancing the oil and biomass yield could significantly improve the probability of economic success and the net present value of the FGB production process. The techno-economic and socioeconomic burden of using genetically modified (GM) strains and the preventive control strategies on the bioeconomy of FGB production is reviewed. It is shown that the fully lined open raceway pond could cost up to 25% more than unlined ponds. The cost of a plastic hoop air-supported greenhouse covering cultivation ponds is estimated to be US 60,000$ /ha. The competitiveness and profitability of large-scale cultivation of GM biomass are significantly locked to techno-economic and socioeconomic drivers. Nonetheless, it necessitates further research and careful long-term follow-up studies to understand the mechanism that affects these parameters the most.
Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle's kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors' knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.
Genetic engineering applications in the field of biofuel are rapidly expanding due to their potential to boost biomass productivity while lowering its cost and enhancing its quality. Recently, fourth-generation biofuel (FGB), which is biofuel obtained from genetically modified (GM) algae biomass, has gained considerable attention from academic and industrial communities. However, replacing fossil resources with FGB is still beset with many challenges. Most notably, technical aspects of genetic modification operations need to be more fully articulated and elaborated. However, relatively little attention has been paid to GM algal biomass. There is a limited number of reviews on the progress and challenges faced in the algal genetics of FGB. Therefore, the present review aims to fill this gap in the literature by recapitulating the findings of recent studies and achievements on safe and efficient genetic manipulation in the production of FGB. Then, the essential issues and parameters related to genome editing in algal strains are highlighted. Finally, the main challenges to FGB pertaining to the diffusion risk and regulatory frameworks are addressed. This review concluded that the technical and biosafety aspects of FGB, as well as the complexity and diversity of the related regulations, legitimacy concerns, and health and environmental risks, are among the most important challenges that require a strong commitment at the national/international levels to reach a global consensus.