Comprehensive Performance Evaluation Research Articles

Critical risk management strategies for the operation of public–private partnerships: a vulnerability perspective of infrastructure projects

PurposeThe success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.Design/methodology/approachVulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.FindingsThe findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.Originality/valueThe findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

Numerical Analysis on Performance Improvement of a Vertical Plate Indirect Evaporative Cooler with Baffles

The performance of the Plate Indirect Evaporative Cooler (PIEC) can be effectively improved by incorporating baffles in the dry channel. However, in the dimensional influence of the baffles on PIEC performance there remains a research gap. In order to investigate the impact of baffle dimensions on the wet bulb efficiency, namely the average heat transfer coefficient and the cooling capacity of the PIEC, this paper proposed and verified a three-dimensional numerical model and method based on the species transport model and the Euler wall film model. At the same time, in order to obtain the equilibrium point between the enhanced heat transfer performance and the additional resistance induced by baffles, a comprehensive performance evaluation index is introduced. The results indicate that, under the same conditions, (1) the baffle effect on PIEC performance is significant at a lower inlet air velocity, and the wet bulb efficiency of the PIEC with baffles can be improved by 22.8%; (2) the baffle effect on PIEC performance is negative if its relative length exceeds 60% or the primary air inlet velocity surpasses 4 m/s under the conditions specified in this paper; and (3) the baffle effect on PIEC performance is significant when its channel height is lower and its channel width is larger, and the wet bulb efficiency of the PIEC with baffles can be improved by 29.3%.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Comprehensive Evaluation of Anti-icing Performance and Operational Efficiency of Superhydrophobic Propellers.

The icing of propellers is a critical factor that affects the safe operation of aircraft. A superhydrophobic surface, with its extremely low wettability, is highly valuable in the field of anti-icing. This study investigates the distribution pattern of ice on propeller surfaces by employing spray coating to create superhydrophobic surfaces. Additionally, it conducts a comparative analysis of thrust and power under icing and non-icing conditions to comprehensively assess the impact of superhydrophobic surfaces on propeller operation efficiency. The results show that superhydrophobic surfaces not only diminish the ice formation area on propeller surfaces but also enhance the traction of propellers under icing conditions, reducing the power consumption of the propeller. The generated thrust can reach up to 1.6 times that of non-superhydrophobic propellers while consuming only one-third of the power. Furthermore, under non-icing conditions, superhydrophobic surfaces exhibit minimal impact on propeller performance.

Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting

Accurate streamflow forecasting is critical in the domain of water resources management. However, the inherently non-stationary and stochastic nature of streamflow poses a significant challenge to achieving accuracy in streamflow forecasting. In this study, we introduce an MVMD-ensembled Transformer model (MVMD-Transformer), which incorporates the MVMD for concurrent time–frequency analysis of streamflow and related potential influencing variables. The model aligns common modes in the decomposition results, ensuring that the different variables corresponding to each mode have the same center frequency. This alignment overcomes frequency mismatches and helps uncover the intrinsic patterns and essential features between streamflow and associated variables. During the forecasting phase, the Transformer component of the MVMD-Transformer model establishes connections among streamflow and other influencing variables across pairs of nodes in each mode. We tested the performance of the MVMD-Transformer model in forecasting streamflow across 1-, 3-, 5-, and 7-day horizons within the Shiyang River, Heihe River, and Shule River basins situated in the Hexi Corridor of Northwest China. The MVMD-Transformer model harnesses MVMD to concurrently decompose both predictor variables (precipitation, air temperature, air pressure, soil moisture) and the response variable (streamflow). Subsequently, the modes drived from the MVMD were fed into the Transformer, serving as the predictive analytics engine, to forecast streamflow. Furthermore, we conducted a comprehensive performance evaluation by comparing the MVMD-Transformer model against four alternatives: the VMD-ensembled Transformer model (VMD-Transformer), CEEMDAN-ensembled Transformer model (CEEMDAN-Transformer), stand-alone Transformer model, and LSTM model. The results indicate that MVMD-Transformer outperformed all other models, achieving Nash-Sutcliffe coefficient (NSE) values exceeding 0.85 in the majority of the forecasting scenarios. This superior performance highlights the proficiency of the MVMD approach in more accurately unraveling the intricate interdependencies between streamflow and its various potential influencing variables, thus significantly improving the precision of streamflow forecasting.

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

Manufacturing Shape-Controllable Flexible PEDOT/rGO Composite Electrodes for Planar Micro-Supercapacitors.

Flexible electronic products, with their characteristics of flexibility and wearability, have attracted significant attention and have become an important direction in the research and development of the electronics industry. Planar micro-supercapacitors (MSCs) with flexible composite electrodes can provide reliable energy support for these products, propelling their further development. The research employed a quick, effective, and environmentally friendly method of laser scribing to create shape-controllable flexible composite electrodes on composite films of Poly(3,4-ethylenedioxythiophene) and graphene oxide (PEDOT/GO), which were subsequently assembled into MSCs. An analysis of the composite electrode morphology, structure, and elemental distribution was conducted through the utilization of SEM, TEM, and XPS techniques. Following this, a comprehensive evaluation of the electrochemical performance of the flexible MSCs was carried out, which included cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), and assessment of cyclic stability. The analysis of the CV results indicated that the MSCs achieved the areal capacitance of 5.78 mF/cm2 at 5 mV/s. After 5000 cycles at a current density of 0.05 mA/cm2, the capacitance retention rate was 85.4%. The high areal capacitance and strong cycle stability of MSCs highlight the potential of PEDOT/reduced graphene oxide (PEDOT/rGO) electrodes in electrode applications.

Development and characterization of self-centering friction dampers with confined shape memory alloy bars

This study proposes a new self-centering, shape memory alloy bar-based device to address the tension-compression asymmetry and limited energy dissipation capacity of similar SMA bar-based devices. The proposed device, named the Confined Superelastic Friction Damper (CSFD), employs buckling-restrained SMA bars to maintain symmetry in force-displacement responses and enhance effectiveness in both tension and compression. The device also integrates a frictional damping mechanism, contributing to increased energy dissipation capacity. First, an optimal annealing condition is investigated for as-received SMA bars to achieve a satisfactory superelastic response. Then, the working and design principles of the CSFD are outlined. The experimental characterization of CSFD devices in various configurations is conducted under an increasing amplitude loading protocol. The devices are tested with different levels of frictional damping to assess the effects of the added frictional forces on device characteristics. Analysis of test results considers parameters such as hysteresis curves, maximum force, equivalent viscous damping, and self-centering efficiency to provide a comprehensive performance evaluation of each device. Findings reveal that while a single buckling-restrained SMA bar device exhibits up to 90 % higher forces in compression than in tension, a CSFD device with two such bars nearly eliminates this asymmetry. Compared to tension-only SMA devices, capable of symmetric response, the proposed CSFD device demonstrates a 2.6 times higher force-resisting capacity. For all devices, the addition of some frictional damping improves the energy dissipation and equivalent viscous damping capacity, while leading to some reduction in self-centering efficiency.

Green lubricants in action: a comprehensive performance evaluation of groundnut oil-based cutting fluids in metal machining processes

As industries worldwide seek environmentally sustainable solutions, the metalworking sector faces a growing need for eco-friendly alternatives to traditional cutting fluids. This abstract introduces the concept of an innovative approach to cutting fluid technology—the use of groundnut oil as a base material for machining fluids. Derived from peanuts, groundnut oil presents a renewable and biodegradable alternative to petroleum-based counterparts, addressing concerns related to resource depletion and environmental impact. A comprehensive performance evaluation of groundnut oil- based cutting fluid has been carried out by series of critical tests such as separation testing, particle size and stability testing, frictional testing, corrosion testing and drilling testing. The results of these tests collectively contribute to a comprehensive understanding of groundnut oil-based cutting fluids, shedding light on their potential as sustainable and high-performance alternatives in metalworking. The zeta potential for the prepared green cutting fluid has been found to be 49.10 mV. The dimensions of the dispersed particles in a fluid of the cutting fluid have been found as 250–260 nm. The environmentally friendly cutting fluid exhibits favourable outcomes in corrosion resistance, frictional performance, and drilling efficacy during testing.

Optimizing Wireless Sensor Networks through Ant Colony-Based Localized Mesh Topology

The growing need for faster and more effective data handling and data transfer capabilities of a network require energy-efficient and more powerful smart sensor devices. These devices have become increasingly necessary in wireless sensor networks (WSN). The architecture or topology of WSN can greatly impact the organizational efficacy and connectivity among the sensor nodes employed within the given area. It is foremost important to implement a robust network topology which must be capable of ensuring continuous and reliable communication within the whole network. This research paper presents an effective approach based on Ant colony scheme to optimize mesh network topology ant colony optimization (ACO) is used to place the sensor nodes optimally in the given area of the whole network. The proposed research carries out comprehensive performance evaluation of the network under various QoS parameters such as bandwidth, throughput, delay, residual energy and routing load. These parameters shows the effectiveness or adaptability of the network with different and dynamically changing communication requirements suggested by this topology scheme. The network based on the proposed method has been simulated several times and the achieved simulation patterns have been analyzed under the mentioned QoS constraints.

Secure and Efficient Covert Communication for Blockchain-Integrated SAGINs

Blockchain has brought great potentials in improving Space-Air-Ground Integrated Networks (SAGINs) in terms of security and efficiency. In blockchain-integrated SAGINs, many applications and services inherently require both the communication contents and communication behaviors to be secure against eavesdroppers, in which a covert communication algorithm is always deployed as a fundamental communication component. However, existing covert communication schemes suffer from critical problems. On the one hand, they require a sender to locally maintain a cryptographic key for a long period of time, which is very costly and inefficient to renew the security protection. On the other hand, the ciphertext of covertly-sent data would explicitly appear in the network, and thereby the schemes are vulnerable to secret key breach. In this paper, we propose a secure and efficient covert communication scheme for blockchain-integrated SAGINs, dubbed CC-BSAGINs, to free the sender from maintaining secret keys. The key technique is to map the covertly-sent data to some transactions on the underlying blockchain in a secure and efficient way; the mapping information is sent via a covert communication algorithm. Such a two-step mechanism releases the sender from key management and does not require the ciphertext to be communicated. We provide formal security proofs and conduct a comprehensive performance evaluation, which demonstrates the security and efficiency of CC-BSAGINs.

Numerical study on cooling characteristics of turbine blade based on laminated cooling configuration with clapboards

The double-wall configuration, characterized by its notable overall cooling effectiveness, serves as a pivotal reference configuration in the design of next-generation turbine blades. In this study, a laminated cooling configuration with clapboards is developed, based on insights derived from both double-wall cooling configuration and laminated cooling configuration. Through a comparative analysis, with the double-wall cooling configuration and laminated cooling configuration as benchmarks, the newly constructed configuration is capable of enhancing overall cooling effectiveness while maintaining control over total pressure loss in the study. Specifically, in the context of external film cooling with an adiabatic condition, the laminated cooling configuration with clapboards attains the highest value at low blowing ratios, while the laminated cooling configuration excels in average adiabatic film cooling effectiveness at high blowing ratios. Importantly, the inclusion of clapboards significantly improves the internal heat transfer area, resulting in a greater internal total heat exchange in the laminated cooling configuration with clapboards. Consequently, this proposed configuration achieves the highest overall cooling effectiveness, achieving increases of 9.02%–14.08 % and 2.77 %–3.54 % relative to the double-wall cooling configuration and laminated cooling configuration, respectively. A comprehensive performance evaluation criterion is applied to assess cooling effectiveness and pressure loss. The results indicate that the laminated cooling configuration with clapboards enhances heat transfer effectiveness while effectively controlling pressure drop, with an evaluation value of over 1.0. Furthermore, the overall cooling effectiveness of the three cooling configurations is compared at two Reynolds numbers. It is found that the proposed configuration exhibits the best cooling performance, especially at the high Reynolds number.

Bidirectional interlayer cooling of 3D chip stack for temperature uniformity enhancement and hotspot mitigation

In this work, bidirectional interlayer cooling with both enhanced temperature uniformity and low flow resistance is proposed for effective 3D chip stack thermal management. Staggered uniform micropin fins and curved entrance and exit regions are elaborately designed. Experimental and numerical studies are carried out to investigate the cooling characteristics under different Reynolds numbers and heat fluxes. In comparison with common interlayer cooling solutions, counter-flow arrangement is achieved between the layers and vertical heat transfer within the chip stack is utilized. Heat fluxes of 300 W/cm2 and 100 W/cm2 are dissipated in the hotspot and background zones respectively. Total thermal resistance of 0.18 K/W is obtained. The maximum decrease in temperature difference is up to 66.2%. Both thermal resistance and coefficient of performance (COP) are considered and compared with the results from previous studies for comprehensive performance evaluation. The maximum COP of 11,523 is reached. The thermal and hydraulic performance advantages of the present design can be verified. The bidirectional interlayer cooling offers new potential for thermal management of 3D chip stack.

Facile fabrication of stain‐resistant poly(lactic acid)/silicone powder fibers: Comprehensive structure characterization and performance evaluation

Facile fabrication of stain‐resistant poly(lactic acid)/silicone powder fibers: Comprehensive structure characterization and performance evaluation

Research needs for enhancing pre-occupancy evaluation of buildings

PurposeMany buildings fail to meet user expectations, causing a performance gap. Pre-occupancy evaluation (PrOE) is believed to have the potential to close the gap. It enables designers to obtain end-user feedback in the design phase and improve the design for better performance. However, PrOE implementation faces challenges due to still maturing knowledgebase. This study aims to understand the state-of-the-art knowledge of PrOE, thereby identifying future research needs to advance the domain.Design/methodology/approachA systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework was conducted. A thorough search in five databases and Google Scholar retrieved 90 articles, with 30 selected for systematic review after eliminating duplicates and irrelevant articles. Bibliometric analyses were performed using VOSviewer and Biblioshiny on the article metadata, and thematic analyses were conducted on their contents.FindingsPrOE is a vehicle for engaging building end-users in the design phase to address the credibility gap caused by the discrepancies between the expected and actual performance of buildings. PrOE has gained limited applications in healthcare, residential, office and educational building design for two broad purposes: design management and marketing. Using virtual reality technologies for PrOE has demonstrated significant benefits. Yet, the PrOE domain needs to mature in multiple perspectives to serve its intended purpose effectively.Originality/valueThis study identifies four knowledge gaps for future research to advance the PrOE domain: (1) developing a holistic PrOE framework, integrating comprehensive performance evaluation criteria, useable at different stages of the design phase and multi-criteria decision algorithms, (2) developing a mixed reality tool, embodying the holistic PrOE framework, (3) formulating a PrOE framework for adaptive reuse of buildings and (4) managing uncertainties in user requirements during the lifecycle in PrOE decisions.

An optimized meta‐heuristic clustering‐based routing scheme for secured wireless sensor networks

SummaryPrivacy and security present significant challenges in wireless sensor networks (WSNs). In order to enhance security, the sensor network is equipped with high throughput. While the importance of both source node (SN) and base station (BS) location privacy and security is acknowledged, recent research has predominantly focused on location privacy. Addressing this gap, the geometric zigzag bidirectional tree effectively tackles privacy and security threats at both the SN and BS locations. Future iterations of WSN are anticipated to integrate additional functionalities to meet diverse requirements in real‐world applications. The absence of robust security constraints leads to an unpredictable sensor network setup. This article aims to reduce energy consumption (EC) while simultaneously enhancing network security and connectivity. To address security challenges in WSN, we propose an optimized meta‐heuristic clustering‐based privacy key‐agreement routing technique. In the suggested system, a gateway‐based network is constructed to devise a key arrangement protocol that promotes privacy during communication. The proposed routing strategy involves forming clusters of sensor nodes (), facilitating the efficient selection of cluster heads (CHs) that prioritize nodes with the least modification. This effectively addresses the EC problem. A comprehensive performance evaluation is conducted, considering improvements in energy efficiency, packet delivery ratio (PDR), throughput, end‐to‐end delay ( delay), and EC.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores’ ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10−19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Typhoon- and temperature-induced responses of a cable-stayed bridge

Long-span bridges are subjected to wind and temperature actions. Wind action is generally the governing load of long-span bridges in design, while temperature action is also significant. Accurate separation of typhoon- and temperature-induced responses is thus necessary for in-depth investigation of their effects and comprehensive evaluation of structural performance. This paper separates the temperature- and typhoon-induced responses of the Qingzhou Bridge by using a unified global analysis approach. The field-monitored meteorological data and structural responses collected from the structural health monitoring system installed on the bridge are analyzed in detail, with emphasis on the comparison of those during typhoon Higos (Signal No. 9) and a typical sunny day after the typhoon. Results show that the quasi-static variation of displacement and stress responses are higher on a typical sunny day than those during the typhoon period, which is out of intuition. Through the unified analysis, the temperature-induced responses are calculated, and the typhoon-induced responses can be separated. The temperature- and typhoon-induced longitudinal displacement, mid-span deflection, and stress of the girder are compared. The temperature-induced response accounts for a large part of the total quasi-static recording either on a typical sunny day or during the typhoon period, whereas typhoon caused significant dynamic responses. The typhoon-induced quasi-static and dynamic responses are also in good agreement with the wind tunnel test results. This case study demonstrates the effectiveness of the unified global analysis in separating the temperature effect.

Dynamic Comprehensive Evaluation of a 660 MW Ultra-Supercritical Coal-Fired Unit Based on Improved Criteria Importance through Inter-Criteria Correlation and Entropy Weight Method

To address the issue of traditional static evaluation models being unable to comprehensively analyze the performance of ultra-supercritical coal-fired units under varying loads, we propose a dynamic comprehensive evaluation model based on the improved Criteria Importance Through Inter-criteria Correlation (CRITIC) method and entropy weight method (EWM). The comprehensive performance evaluation index system of ultra-supercritical coal fired units is constructed by examining the boiler performance, turbine performance, plant power performance, environmental performance, and flexible performance of coal-powered units. The CRITIC and EWM methods are used to calculate the weights of the indicators, which are then combined with the static evaluation results. Using a dynamic comprehensive evaluation model, we analyze ultra-supercritical coal-fired units, taking into account time weight. This allows us to obtain the comprehensive dynamic real-time evaluation value of the units under different loads. The research indicates that the weight of the evaluation index is changed when using the dynamic comprehensive evaluation model of the improved CRITIC and EWM. The index with lower weight is increased by 6.2%, while the index with higher weight is decreased by 0.22%. This alteration in weight range can provide a more objective reflection of the relationship between evaluation indicators. This model offers significant advantages in improving evaluation accuracy, weight balance distribution, and generality.

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.