Search results

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

A risk-based method is proposed to identify the dominant influencing factors of secondary lining cracking in an operating mountain tunnel with weak surrounding rock.

Based on the inspection data from a mountain tunnel in Southwest China, a lognormal proportional hazard model is established to describe the statistical distribution of secondary lining cracks. Then, the model parameters are obtained by using the Bayesian regression method, and the importance of influencing factors can be sorted based on the absolute values of the parameters.

The results show that the order of importance of the influencing factors of secondary lining cracks is as follows: location of the crack on the tunnel profile, rock mass grade of the surrounding rock, time to completion of the secondary lining, and void behind the secondary lining. Accordingly, the location of the crack on the tunnel profile and rock mass grade of the surrounding rock are the two most important influencing factors of secondary lining cracks in the investigated mountain tunnel, and appropriate maintenance measures should be focused on these two aspects.

This study provides a general and effective reference for identifying the dominant influencing factors of secondary lining cracks to guide the targeted maintenance in mountain tunnels.

This study aims to characterize the mechanical properties of sintered nano-silver under various sintering processes by nano-indentation tests.

Through microstructure observations and characterization, the influences of sintering process on the microstructure evolutions of sintered nano-silver were presented. And, the indentation load, indentation displacement curves of sintered silver under various sintering processes were measured by using nano-indentation test. Based on the nano-indentation test, a reverse analysis of the finite element calculation was used to determine the yielding stress and hardening exponent.

The porosity decreases with the increase of the sintering temperature, while the average particle size of sintered nano-silver increases with the increase of sintering temperature and sintering time. In addition, the porosity reduced from 34.88%, 30.52%, to 25.04% if the ramp rate was decreased from 25°C/min, 15°C/min, to 5°C/min, respectively. The particle size appears more frequently within 1 µm and 2 µm under the lower ramp rate. With reverse analysis, the strain hardening exponent gradually heightened with the increase of temperature, while the yielding stress value decreased significantly with the increase of temperature. When the sintering time increased, the strain hardening exponent increased slightly.

The mechanical properties of sintered nano-silver under different sintering processes are clearly understood.

This paper could provide a novel perspective on understanding the sintering process effects on the mechanical properties of sintered nano-silver.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

Ferrofluids are aqueous or non-aqueous solutions with colloidal particles of iron oxide nanoparticles with high magnetic characteristics. Their magnetic characteristics enable them to be controlled and manipulated when ferrofluids are exposed to magnetic fields. This study aims to inspect the features of unsteady stagnation point flow (SPF) and heat flux from the surface by incorporating ferromagnetic particles through a special kind of second-grade fluid (SGF) across a movable sheet with a nonlinear heat source/sink and magnetic field effect. The mass suction/injection and stretching/shrinking boundary conditions are also inspected to calculate the fine points of the features of multiple solutions.

The leading equations that govern the ferrofluid flow are reduced to a group of ordinary differential equations by applying similarity variables. The converted equations are numerically solved through the bvp4c solver. Afterward, study and discussion are carried out to examine the different physical parameters of the characteristics of nanofluid flow and thermal properties.

Multiple solutions are revealed to happen for situations of unsteadiness, shrinking as well as stretching sheets. Greater suction slows the separation of the boundary layers and causes the critical values to expand. The region where the multiple solutions appear is observed to expand with increasing values of the magnetic, non-Newtonian and suction parameters. Moreover, the fluid velocity significantly uplifts while the temperature declines due to the suction parameter.

The novelty of the work is to deliberate the impact of mass suction/injection on the unsteady SPF through the special second-grade ferrofluids across a movable sheet with an erratic heat source/sink. The confirmed results provide a very good consistency with the accepted papers. Previous studies have not yet fully explored the entire analysis of the proposed model.

This study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.

The study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.

It is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.

Limited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

The current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.

The constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.

The proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.

Introducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.