Search Results (10)

This study focuses on the East Channel Project (Xiang’an South Road—Airport Expressway Section). The project is in the South Port Harbor Bay area. The area has highly complex and asymmetrical geology. Construction faces multiple challenges: tight schedule, overlapping pipeline operations, and large-scale foundation treatment needs. To tackle these, the project uses the plastic drainage board surcharge preloading method for ground improvement. This technique needs continuous settlement deformation monitoring. The monitoring aims to spot potential asymmetric trends and fix the best unloading time. Traditional settlement prediction methods have limits. So, this study develops an intelligent prediction model (SSA-BP). It combines the Sparrow Search Algorithm (SSA) with the BP neural network. The model uses SSA’s strong global search ability to optimize the BP network’s initial weights and thresholds. This effectively avoids local minima and improves prediction stability. Comparative experiments with other optimization algorithms (Particle Swarm Optimization PSO, Grey Wolf Optimizer GWO, and Differential Evolution DE) show that the SSA-BP model has better convergence accuracy and robustness. Field monitoring data validation indicates the model’s prediction error is stably between −3.4% and 3.2%. It surpasses traditional methods like the three-point and hyperbolic methods. The study’s key innovation is introducing an asymmetry-aware view. It analyzes settlement’s morphological evolution and predictability under surcharge preloading. The SSA-BP model can identify both symmetric and asymmetric deformation patterns well. It offers a new computational tool to understand asymmetry breaking in geotechnical systems. Moreover, the model can accurately predict settlement behavior in real time. This provides dynamic construction decision-making guidance and effective cost control. This research shows that intelligent algorithms have great potential. They can reveal complex geotechnical systems’ inherent laws and promote foundation engineering’s intelligentization. Full article

The shallow-water delta-front reservoir in Member II of the Oligocene Dongying Formation (Ed2), located in an oilfield within the Bohai Bay Basin, is a large-scale composite sedimentary system dominated by subaqueous distributary channels and mouth bars. Within this system, reservoir sand bodies exhibit significant thickness, complex internal architecture, poor injection–production correspondence during development, and an ambiguous understanding of remaining oil distribution. To enhance late-stage development efficiency, it is imperative to deepen the understanding of the genesis and evolution of the subaqueous distributary channel–mouth bar system, analyze the internal reservoir architecture, and clarify sand body connectivity relationships. Based on sedimentary physical modeling experiments, integrated with core, well logging, and seismic data, this study systematically reveals the architectural characteristics and spatial stacking patterns of the mouth bar reservoirs using Miall’s architectural element analysis method. The results indicate that the study area is dominated by sand-rich, shallow-water delta front deposits, which display a predominantly coarsening-upward character. The main reservoir units are mouth bar sand bodies (accounting for 30%), with a vertical stacking thickness ranging from 3 to 20 m, and they exhibit lobate distribution patterns in plan view. Sedimentary physical modeling reveals the formation mechanism and stacking patterns of these sand-rich, thick sand bodies. Upon entering the lake, the main distributary channel unloads its sediment, forming accretionary bodies. The main channel then bifurcates, and a new main channel forms in the subsequent unit, which transports sediment away and initiates a new phase of deposition. Multi-phase deposition ultimately builds large-scale lobate complexes composed of channel–mouth bar assemblages. These complexes exhibit internal architectural styles, including channel–channel splicing, channel–bar splicing, and bar–bar splicing. Reservoir architecture analysis demonstrates that an individual distributary channel governs the formation of an individual lobe, whereas multiple distributary channels control the development of composite lobes. These lobes are laterally spliced and vertically superimposed, exhibiting a multi-phase progradational stacking pattern. Dynamic production data analysis validates the reliability of this reservoir architecture classification. This research elucidates the genetic mechanisms of thick sand bodies in delta fronts and establishes a region-specific reservoir architecture model. This study clarifies the spatial distribution of mudstone interlayers and preferential flow pathways within the composite sand bodies. It provides a geological basis for optimizing injection–production strategies and targeting residual oil during the ultra-high water-cut stage. The findings offer critical guidance for the efficient development of shallow-water delta front reservoirs. Full article

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics. Full article

The industry usually faces a problem during the loading/unloading of finished products and raw materials from one place to another when both places are at different elevations. As trucks are of variable height and industry loading bays are at different elevations, it is not possible to drive the pallets effectively into freight, which results in decreasing loading/unloading efficiency of small concerns. In this paper, an adjustable height ramp system for increasing production efficiency and improving the industrial working environment was developed using a linear actuator and automation system for the safe loading and unloading of pallets. This adjustable ramp will help to increase the productivity of micro, small and medium enterprises (MSMEs), and it will provide a safe working environment. Using an adjustable ramp will help create a bridge between industry loading bays and freight, and it will also resolve the issue of different heights of both by making a path between them. The Internet of things (IoT)-enabled lifting and downward movement of the ramp is attempted for oil/air filter MSMEs. Full article

During horse transportation, the journey conditions are considered a welfare risk. This study aimed to document journeys, journey conditions, and welfare status of handled horses on arrival at two different slaughterhouses in Northern and Southern Italy, to find possible associations between journey conditions and welfare problems. The welfare status of 613 draft-breed and light-breed horses from 32 different journeys was evaluated on arrival at the slaughterhouses with a standardized protocol, using animal-based (ABMs) and environmental-based (EBMs) measures. The drivers’ skills and vehicle characteristics were found to be mostly compliant with EC 1/2005. The horses traveled in single bays, 90° to the direction of travel for an average journey duration of 26.5 ± 14 h. On arrival at the slaughterhouses, the horses were unloaded by handlers, via halter and rope. The prevalence of reluctance to unload, injuries, nasal, and lacrimal discharge was 22.2%, 24.6%, 11.6%, and 10%, respectively. Journey duration, unloading duration, vehicle changes, long stops, handlers/drivers’ skills, temperature, season, and horse individual characteristics were associated with horses’ welfare and health status (all p < 0.05). Our study confirms the hypothesis that appropriate journey conditions are of crucial importance to safeguard the welfare of broken/handled horses transported over long distances for slaughter. Full article

The container ship stowage planning problem (CSPP) is a very complex and challenging issue concerning the interests of shipping companies and ports. This article has developed a many-objective CSPP solution that optimizes ship stability and reduces the number of shifts over the whole route while at the same time considering realistic constraints such as the physical structure of the ship and the layout of the container yard. Use the initial metacentric height (GM) along with the ship’s heeling angle and trim to measure its stability. Meanwhile, use the total amount of relocation in the container terminal yard, the voluntary shift in the container ship’s bay, and the necessary shift of the future unloading port to measure the number of shifts on the whole route. This article proposes a variant of the nondominated sorting genetic algorithm III (NSGA-III) combined with local search components to solve this problem. The algorithm can produce a set of non-dominated solutions, then decision-makers can choose the best practical implementation based on their experience and preferences. After carrying out a large number of experiments on 48 examples, our calculation results show that the algorithm is effective compared with NSGA-II and random weighted genetic algorithms, especially when applied to solve many-objective CSPPs. Full article

Usually, loading and unloading of cargo ships takes place in ports that are equipped with the infrastructure necessary to carry out such operations. In the Arctic, often a helicopter is the only way to get the cargo to the right place. Finding the optimal geographic location for unloading a ship using helicopters is an important task. It is necessary to create a support system for making the right decisions in such situations. Mathematical modeling has been used to find the geographical location that ensures the most favorable and quickest delivery of cargo from a vessel to its destination, using a helicopter. A criterion has also been found in which the search for the optimum point is a more rational way of unloading the vessel compared to other discharge options. The maps of the economic benefits of loading and unloading operations in this model have been developed. Using the example of the developed model, it is shown that during the transportation of goods in Ob Bay, significant economic and temporary advantages can be obtained. The developed model can be extended to the case of cargo delivery not only in the Arctic conditions, but also where the transport infrastructure is insufficiently developed. Full article

The joint scheduling of quay cranes (QCs), yard cranes (YCs), and yard trucks (YTs) is critical to achieving good overall performance for a container terminal. However, there are only a few such integrated studies. Especially, those who have taken the vessel stowage plan (VSP) into consideration are very rare. The VSP is a plan assigning each container a stowage position in a vessel. It affects the QC operations directly and considerably. Neglecting this plan will cause problems when loading/unloading containers into/from a ship or even congest the YT and YC operations in the upstream. In this research, a framework of simulation-based optimization methods have been proposed firstly. Then, four kinds of heuristics/metaheuristics has been employed in this framework, such as sort-by-bay (SBB), genetic algorithm (GA), particle swarm optimization (PSO), and multiple groups particle swarm optimization (MGPSO), to deal with the yard crane scheduling problem (YCSP), yard truck scheduling problem (YTSP), and quay crane scheduling problem (QCSP) simultaneously for export containers, taking operational constraints into consideration. The objective aims to minimize makespan. Each of the simulation-based optimization methods includes three components, load-balancing heuristic, sequencing method, and simulation model. Experiments have been conducted to investigate the effectiveness of different simulation-based optimization methods. The results show that the MGPSO outperforms the others. Full article

The problem of urban logistics operations in the context of their impact on the environment has become the key challenge. Due to that, there has been a growing interest in increasing the use of alternative fuels, including electro-mobility. However, an important barrier to the utilisation of electric freight vehicles (EFVs) is their travel range and battery capacity. The paper is focused on the idea of EFV utilisation improvement by implementation of charging stations in unloading bays. First, the Authors analysed the efficiency of chosen vehicles during daily work. Next, the potential improvement of their travel range was analysed, considering the short-time charging processes carried out during delivery operations, using the charging systems provided in unloading bays. Moreover, the concept of wireless chargers utilisation was proposed as a challenge for future work. According to the analysis, utilisation of unloading bays equipped with short-time battery chargers could improve significantly the travel range of EFVs. As a result, it could improve the efficiency of electric vehicles in last mile deliveries in city areas. Full article

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility. Full article