Search Results (167)

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more; the duration is shortened by 18 days, a reduction of 21%; and the total cost is reduced by CNY 220,000, saving 3% of the cost. Full article

The acceleration of urbanization has led to increasingly severe traffic congestion, creating an urgent need for effective traffic signal control strategies to improve road efficiency. This paper proposes an adaptive traffic signal control method based on offline reinforcement learning (Offline RL) to address the limitations of traditional fixed-time signal control methods. By monitoring key parameters such as real-time traffic flow and queue length, the proposed method dynamically adjusts signal phases and durations in response to rapidly changing traffic conditions. At the core of this research is the design of a model named SD3-Light, which leverages advanced offline reinforcement learning to predict the optimal signal phase sequences and their durations based on real-time intersection state features. Additionally, this paper constructs a comprehensive offline dataset, which enables the model to be trained without relying on real-time traffic data, thereby reducing costs and improving the model’s generalization ability. Experiments conducted on real-world traffic datasets demonstrate the effectiveness of the proposed method in reducing the average travel time. Comparisons with several existing methods highlight the clear advantages of our approach in enhancing traffic management efficiency. Full article

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation. Full article

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources. Full article

High-strength steels are widely used in various mechanical production and construction industries for their low cost, high strength and high toughness. Among these, bainitic steels have better comprehensive performance relative to martensite and ferrite. In this paper, from the point of view of its microscopic fine structure and mechanical properties, the high-carbon silicon-containing steel Fe-0.99C-1.37Si-0.44Mn-1.04Cr-0.03Ni was austenitized at high temperature after a brief isothermal treatment at 280 °C and is briefly reviewed. We have used EBSD, TEM and 3D-APT to observe a unique transformation in which high-carbon silicon-containing steels form nanostructured bainite with nanometer widths. Intriguingly, as the isothermal duration decreases, the beam bainite width becomes increasingly finer. When the beam bainite width falls below 50 nm, there is a sudden shift in defect type from the conventional edge-type dislocations to a defect characterized by the insertion of a semi-atomic surface in the opposite direction, which leads to different degrees of reduction in the micro- and macro-mechanical properties of high-carbon silicon-containing steels from 1754 MPa to 1667 MPa. This sudden change in the sub-structural properties is typical of the reverse Hall–Petch effect. Full article

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors. Full article

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. Full article

Positive nursing practice environments benefit clients, professionals, and institutions, specifically for professionals, by improving professional visibility, recognition, retention intentions, well-being, commitment, job satisfaction, and reducing stress and burnout. Clients experience better quality care, safety, satisfaction, engagement, and fewer care omissions. Institutions favored reduced absenteeism and turnover, improved efficiency, lower costs, and better health outcomes. This study aimed to document the development of the “Program to Promote Positive Nursing Practice Environments” and analyze expert perceptions to reach a consensus. Using a two-round modified online Delphi technique with 22 experts, we examined perspectives on the program’s goals, duration, organization, and content. Initial feedback showed agreement on the program’s duration but mixed opinions on the number and length of sessions due to concerns about participant workload and fatigue. Adjustments led to a strong consensus and positive feedback on the program’s coverage of essential nursing practice environment constructs. The program includes sessions on client, professional, and institutional dynamics, focusing on key attributes and outcomes for fostering positive nursing environments. By equipping nurses with knowledge and skills, the program aims to enhance work environments effectively. These findings provide a substantial advancement in the field, offering a robust framework for future initiatives. Full article

This study explores integrating Building Information Modeling (BIM) and Value Engineering (VE) to enhance cost management and project value optimization in residential construction. The primary aim is to develop a comprehensive framework that synergizes 5th Dimension Building Information Modeling (5D BIM) with VE processes to identify and evaluate the most cost-effective construction alternatives. Employing a mixed-methods approach, this research includes an extensive review of existing practices, an integrated BIM-VE framework proposal, and the application of 5D BIM principles to improve visualization, cost estimation, and scheduling. The findings reveal that integrating BIM and VE significantly enhances project efficiency, quality, and cost-effectiveness. Specifically, the case study of a twin villa project demonstrates a 42% reduction in floor material costs and a 30% reduction in door material costs, resulting in an overall project cost savings of 35%. Additionally, the integrated approach contributes to a 15% reduction in project duration and a notable improvement in design quality and stakeholder collaboration. This research contributes significantly by providing a robust framework for BIM and VE integration, emphasizing its potential to revolutionize cost management practices in the construction industry. The novelty of this study lies in its detailed and practical approach to merging BIM with VE, offering a viable solution for resource optimization and sustainable building practices. This study highlights the transformative potential of BIM-VE integration, advocating for its broader adoption to achieve superior project outcomes. Full article

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness. Full article

Lightweight engineered trusses support sustainable construction with the benefits of mass production and fast construction at lower costs. However, the truss system has raised concerns due to premature failure in fire conditions. This study investigates the effect of a thin soot layer on the surface of the gusset plate and the teeth of the gusset plate on the temperature development within lightweight wood specimens in fire conditions. A 10 cm long, 8.9 cm wide, and 3.8 cm thick dimensional lumber (often called 2 by 4) partially covered by a gusset plate was exposed to a constant incident radiant heat flux. A total of 12 experiments were conducted with four different configurations, bare gusset plates with and without teeth and soot-coated gusset plates with and without teeth, at three different external radiative heat fluxes of 10, 15, and 20 kW/m². The exposure durations were set to be 60, 40, and 30 min, respectively, to allow the total applied amount of radiant energy for each specimen to be identical. Three thermocouples were installed at a depth of 13 mm from the exposed wooden surface: two beneath the gusset plate and one below the uncovered wooden surface, and an additional thermocouple was between the gusset plate and the wood surface. The obtained temperature data showed that soot-coated gusset plates absorb significantly more radiation and record higher temperatures within the specimens than the specimens with the bare gusset plates. It was also found that the bare gusset plate works as a protective layer for the wood at 20 kW/m², but not at 10 and 15 kW/m². The teeth certainly contributed to heat transfer increasing the temperatures within the wood higher than those without teeth, but the effect was only meaningful for the soot-covered specimens. Connection strength was also qualitatively analyzed and it was discovered that the bare specimen retained a strong connection between the gusset plate and wood. In contrast, the soot-coated specimen was easily removed by hand, even when exposed to the same heat flux. Applying these results to a realistic scenario, this loss in connection strength could result in truss failure and structural collapse, which may result in injury to or even death of the responding firefighters. Additional gusset plate protection measures may be necessary to prolong the connection strength and prevent structural collapse. Full article

As maritime technology advances, exploration of the oceans has progressively moved from surface exploration to underwater ventures. Unmanned underwater vehicles (UUVs), now prevalent for such exploration, effectively reduce human labor and lower operational costs. These vehicles rely on an internal Battery Storage System (BSS) that sustains device operation by extending operational duration and providing stable voltage. Typically arranged in series, BSSs face challenges due to differences in the chemical characteristics of individual batteries, which lead to discrepancies in battery voltages and cause imbalances during charge and discharge cycles. This results in varied utilization rates among the batteries and uneven aging of the battery pack, potentially decreasing operational efficiency and increasing failure rates, thus reducing reliability and safety. Considering the harsh environmental conditions and maintenance difficulties associated with underwater operations, this paper proposes a robust solution: a balancing system featuring a modular switch with electrical isolation. Through theoretical analysis and circuit simulation, this study constructs and tests a novel prototype of a capacitor-based equalizer circuit with electrical isolation, verifying its feasibility. Full article

The selection of an appropriate construction method stands as a pivotal decision in ensuring the success of any building project. This paper undertakes a comprehensive comparative analysis of cost and timeline implications between the top-down and bottom-up construction methodologies. The research focuses on ten distinct underground structure models, each characterized by varying depths and base areas. Through rigorous design and analysis, the cost and projected duration for each model are meticulously evaluated under the lens of both top-down and bottom-up construction techniques. The findings reveal that while the bottom-up approach emerges as the most cost-effective alternative at a depth of 8 m, the top-down method exhibits superior cost efficiency with increasing depth. Interestingly, alterations in the structure’s base area exert a minimal influence on the cost differentials between the two methodologies. Moreover, the top-down construction method consistently outshines its counterpart in terms of project duration across all ten models examined, with the percentage increase in base area yielding insignificant impacts on project timeline discrepancies. This study furnishes construction project managers with invaluable insights to navigate the complexities of method selection. By judiciously considering parameters such as project scale, depth, and base area, managers can strategically optimize both cost and timeline outcomes, thereby facilitating the seamless execution of construction projects. Full article

The construction industry presents a significant environmental challenge due to its substantial environmental footprint, utilization of limited natural resources, and contribution to pollution and climate change. Additionally, optimizing the weight, cost, and duration of construction is crucial for enhancing serviceability, flexibility, efficiency, and profitability. In this research, the relationship between structure weight and other objective functions was explored using the single-objective gray wolf algorithm to investigate their impact on carbon footprint, water footprint, and construction time. Furthermore, employing a multi-objective optimization algorithm, a building structure was optimized for three systems featuring different structural frames based on the specified objective functions. The results revealed that the structure with intermediate steel moment-resisting frames exhibited the shortest construction time but incurred the highest construction cost. Conversely, the structure with intermediate steel moment-resisting frames with special steel concentric bracing demonstrated the lowest carbon footprint and water footprint among the studied structural frames. Consequently, the structure with intermediate steel moment-resisting frames with special concentric steel bracing was proposed as a green structure, emphasizing its environmentally friendly characteristics. Full article

Construction projects are complexity, multidisciplinary, have thousands of activities and details, and involve many participants. This intricate and fragmented nature of construction projects coupled with tight budgets and limited resources makes them good candidates for failure and promotes the emergence of risks. These risks must be addressed in the decision-making process and properly managed to mitigate their effect. However, risk management is one of the most difficult tasks and the assessment and analysis of the cost and schedule risks of construction activities are considered the most challenging tasks in the whole risk management process and require careful considerations throughout the life cycle of a project. Despite the high cost associated with managing risk in construction projects, the outcome of this task normally provides fruitful benefits. To address this crucial issue, this study employs a mixed methodology approach utilizing both qualitative and quantitative methods to gather feedback from construction experts and identify schedule and cost risk events associated with construction activities, prioritize their likelihood of occurrence, and suggest responses to mitigate them. The Program Evaluation and Review Technique (PERT) and earned value (EV) analysis are then used to estimate the expected cost at completion and the risk associated with it. A spreadsheet framework is then developed to help construction practitioners identify the most severe risks and measure their effect on the project’s duration and cost. The framework also suggests risk responses for each of the risk events. The study then provides recommendations to mitigate risks with high impact and severity. Full article