Dynamic Feedback Research Articles

A Comprehensive Review of Dynamic Speed Feedback Signs in Reducing Speed at Different Critical Locations

A Comprehensive Review of Dynamic Speed Feedback Signs in Reducing Speed at Different Critical Locations

Mixed particle swarm optimization algorithm-based approach to optimize spatial distribution of virtual maize

Increasing crop yields is one potential way to solve the problem of food shortage and limited field resources. Optimization of crop spatial distribution is an efficient method to increase yield. However, extensive field experiments are costly and time consuming, which renders conventional optimization studies difficult if not prohibitive. In this study, a new optimization strategy was proposed, integrating a Functional-Structural Plant Model (FSPM) of maize with a workflow based on a Mixed Particle Swarm Optimization (MPSO) algorithm. The maize FSPM applies a dynamic interactive feedback method between plant morphology and plant growth dynamics on the one hand, and simultaneously between plant spatial distribution and plant growth, and furthermore can respond to different environmental factors. MPSO is a new mixed particle swarm optimization algorithm with multistage disturbance, which enhances the ability of traversing the solution space and jumping out of local extrema. This study performs a plant spatial distribution optimization of monocropping maize to maximize light interception. Simulation results indicate that the optimized row spacing can increase maize light interception comparing to the random row spacing within the spacing range (with maximum of 36.44% increment), and that the optimization results satisfy the optimal interval provided by previous field experiments.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

Rotor Speed Prediction Model of Multi-Rotor Unmanned Aerial Spraying System and Its Matching with the Overall Load

During continuous spraying operations, the liquid in the pesticide tank gradually decreases, and the flight speed changes as the route is altered. To maintain stable flight, the rotor speed of a multi-rotor unmanned aerial spraying system (UASS) constantly adjusts. To explore the variation law of rotor speed in a multi-rotor UASS under objective operation attributes, based on indoor and outdoor experimental data, this paper constructs a mathematical model of the relationship between rotor speed and thrust. The model fitting parameter (R2) is equal to 0.9996. Through the neural network, the rotor speed prediction model is constructed with the real-time flight speed and the payload of the pesticide tank as the input. The overall correlation coefficient (R2) of the model training set is 0.728, and the correlation coefficients (R2) of the verification set and the test set are 0.719 and 0.726, respectively. Finally, the rotor speed is matched with the load of the whole UASS through thrust conversion. It is known that the single-axis load capacity under full-load state only reaches about 50% of its maximum load capacity, and the load increase is more than 75.83% compared with the no-load state. This study provides a theoretical and methodological reference for accurately predicting the performance characterization results of a power system during actual operation and investigating the dynamic feedback mechanism of a UASS during continuous operation.

A remaining useful life prediction framework with adaptive dynamic feedback

Remaining useful life (RUL) prediction of components is a crucial aspect of mechanical systems prognostic and health management. However, during the online RUL prediction of components, the existing method cannot use the prediction results for feedback and optimize the prediction model. To address this issue, this paper proposes a framework for RUL prediction with adaptive dynamic feedback. First, a feedback multi-feature fusion (FMFF) method is designed. It introduces a series of non-exceeding threshold positive constants, that is fraction thresholds (FT), to perform division of the degradation state dimension. Besides, the first hitting time (FHT) from the current moment to FT is the fraction remaining useful life (FRUL). It is used to construct and update the fusion factors. Then, the feedback multi-model selection (FMMS) method is proposed for adaptive feedback and to select the degradation model based on the FRUL prediction results in different stages. The probability density function of RUL based on the FHT is calculated based on the FMMS method to realize the RUL prediction of the component. Finally, the effectiveness and superiority of the proposed framework are validated based on XJTU-SY dataset and run-to-failure test.

Strategic interventions for bridging the design-reality gap in property conferment rights: a case study of India’s ambitious initiative

PurposeThis research aims to assess how well the e-governance initiative aligns with its design expectations and on-ground realities for property management to redevelop Delhi, the mega-city of India. Additionally, the study proposes strategic interventions to address any gaps identified, aiming to improve the project’s effectiveness and success.Design/methodology/approachThe research employs a singular exploratory case study methodology to scrutinise the e-governance initiative undertaken by the Government of India to confer property rights. Specifically, the study utilises a qualitative research approach known as design-reality gap (DRG) analysis. The study draws from primary and secondary sources using a mixed-methods data collection strategy. Primary data are gathered through a purposeful and snowball sampling method involving direct engagement with respondents, whilst secondary data are sourced from the project portal.FindingsThe investigation reveals a substantial disparity between the planning and execution phases of e-governance projects. This incongruity predominantly manifests in the domains of process, staffing and skills, as well as objectives and values. The study further finds that strategic collaboration amongst stakeholders and the sustainability of decisions are the most critical factors in ensuring the success of e-governance initiatives and bridging the DRG of e-governance projects.Research limitations/implicationsThis research highlights the complex challenges faced by e-governance projects in technical, human and organisational aspects. The successful implementation and sustainability of these projects require effective strategies to overcome challenges, which management should proactively identify and act on. To improve services, beneficiaries should be encouraged to provide feedback and suggestions, as they play a crucial role in service enhancement. A dynamic feedback loop would be established by creating a two-way interaction between beneficiaries and service providers, leading to iterative service improvement. It is important to note that the study’s findings are more illustrative than conclusive due to the moderate sample size, reflecting its limitations.Originality/valueThe research contributes to the scholarly discourse on e-governance and policy implementation by comprehensively examining the alignment between conceptual design and real-world execution. It also introduces a normalised scale for the DRG framework, mapping its dimensions to deduce specific outcomes. This innovative approach enhances the research’s originality and value, offering insights applicable in both academic and practical domains.

Prolonged Multi‐Phase Magmatism Due To Plume‐Lithosphere Interaction as Applied to the High Arctic Large Igneous Province

AbstractThe widespread High Arctic Large Igneous Province (HALIP) exhibits prolonged melting over more than 50 Myr, an observation that is difficult to reconcile with the classic view that large igneous provinces (LIPs) originate from melting in plume heads. Hence, the suggested plume‐related origin and classification of HALIP as a LIP have been questioned. Here, we use numerical models that include melting and melt migration to investigate a rising plume interacting with lithosphere of variable thickness, that is, a basin‐to‐craton setting applicable to the Arctic. Models reveal that melt migration introduces significant spatial and temporal variations in melt volumes and pulses of melt production, including protracted melting for at least about 30–40 Myr, because of the dynamic feedback between migrating melt and local lithosphere thinning. For HALIP, plume material deflected from underneath the Greenland craton can re‐activate melting zones below the previously plume‐influenced Sverdrup Basin after a melt‐free period of about 10–15 Myr, even though the plume is already ∼500 km away. Hence, actively melting zones do not necessarily represent the location of the deeper plume stem at a given time, especially for secondary pulses. Additional processes such as (minor) plume flux variations or local lithospheric extension may alter the timing and volume of HALIP pulses, but are to first order not required to reproduce the long‐lived and multi‐pulse magmatism of HALIP. Since melting zones are always plume‐fed, we would expect HALIP magmatism to exhibit plume‐related trace element signatures throughout time, potentially shifting from mostly tholeiitic toward more alkalic compositions.

The challenge of estimating global termite methane emissions.

Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Dynamic effects of path dependence on regional entrepreneurship in transitional China

ABSTRACT Previous studies fall short when explaining the determinants of regional entrepreneurship in an unstable environment from a dynamic perspective. This study investigates the dynamic feedback effects of path dependence on the regional entrepreneurial activities in China. Using a sample of panel data related to 47,515,084 enterprises from 2000 to 2018, we estimate a set of panel models analyzing the effects of dynamic increasing returns and formal and informal institutional hysteresis on the new firm formation in Chinese cities. Our results provide evidence of positive relationships between peer effects from past entrepreneurial activities, past FDI and government intervention as well as the historical environment of the proportion of foreign-funded enterprises and SOEs in 1989 with the start-up rates. This study on regional entrepreneurship in the Chinese context develops a broader understanding of entrepreneurship in transition economies.

Intelligent robust control for nonlinear complex hydro-turbine regulation system based on a novel state space equation and dynamic feedback linearization

To achieve optimal control under all operating conditions for hydropower units, an intelligent robust controller (NSFLIRC) is proposed based on a novel state space equation and state-dynamic-measurement feedback linearization. Firstly, a nonlinear hydro-turbine regulation system (HTRS) model is constructed and a novel high-order state space equation model of HTRS is derived considering system tracking deviation. Then, the robust control for HTRS is achieved by combining the novel high-order state space equation and H∞ control. Furthermore, the slime mold algorithm (SMA) is improved by incorporating hybrid chaotic mapping functions, Levy flight, and a nonlinear weight coefficient. The intelligent optimization of H∞ controller parameters is realized using the improved SMA (ISMA). Finally, a nonlinear HTRS simulation platform is constructed to verify the performance of the proposed NSFLIRC. The simulation results show that when the overshoot is within a reasonable range, using NSFLIRC can respectively reduce the average values of adjusting time, rising time, overshoot, and power inversion by 46.6 %, 29.06 %, and 52.56 % comparing to the traditional PID controller under six typical operating conditions; also, the NSFLIRC has low sensitivity to changes in system parameters and most operating conditions, and has strong robustness under conventional load changes and extreme three-phase short circuit conditions.

Mid-Holocene West African monsoon rainfall enhanced in EC-Earth simulation with dynamic vegetation feedback

Proxy records have shown that the Mid-Holocene was a period of humid conditions across West Africa, with an enhanced West African Monsoon (WAM) and vegetated conditions in areas currently characterized by desert, often referred to as the Green Sahara. However, General Circulation Models regularly struggle with recreating this strengthened Mid-Holocene monsoon in West Africa. Vegetation feedbacks has long been viewed as an essential process modulating the monsoon variability in West Africa, and simulations using prescribed vegetation to recreate a Green Sahara have shown a strengthened WAM and increased rainfall. However, simulations with prescribed vegetation in Sahara represent an idealized vegetation cover and do not take any environmental heterogeneity into account. Furthermore, this only represents a one-directional forcing by the vegetation on the climate rather than the full vegetation feedback. To address this, we have simulated the Mid-Holocene (~ 6 ka) climate using the Earth System Model EC-Earth3-Veg. The results show that coupled dynamic vegetation reproduces an apparent enhancement of the WAM, with the summer rainfall in the Sahel region increasing by 15% compared to simulations with a prescribed modern vegetation cover. Vegetation feedbacks enhance the warming of the Sahara region, deepens the Sahara Heat Low, results in increased rainfall and strengthens monsoonal flow across West Africa. However, the enhancement is still below what can be viewed in proxy reconstructions, highlighting the role of model limitation and biases and the importance of investigating other processes, such as the interactive aerosol-albedo feedback.

Fluvial biogeomorphological feedbacks from plant traits to the landscape: Lessons from selected French rivers in line with A.M. Gurnell's influential contribution

AbstractResearch in fluvial biogeomorphology largely aims to promote our understanding of the interactions between riparian vegetation and fluvial morphodynamics within riverine ecosystems. Starting at the end of the last century, Angela M. Gurnell has made a major contribution to fluvial geomorphology by considering water flow and sediment transport in combinaton with riparian and later also aquatic vegetation and thus significantly promoting the fluvial biogeomorphological approach from its early days until today. The objective of the present paper is to present a set of studies and results obtained over the last 20 years mainly by the authors and many collaborators, including Angela M. Gurnell, on a panel of French rivers: the Tech, Garonne, Isère and Allier. In particular, feedback mechanisms between fluvial morphodynamics and riparian vegetation dynamics were investigated directly in the field and by using high‐resolution remote sensor systems at the scale of individual plants, populations, communities and landscapes as well as during semi‐controlled ex‐situ experiments at the scale of individual plants. Collectively, the authors' research conducted over the past 20 years has helped to elucidate some key aspects of the feedback dynamics between the lowest and highest levels of the riparian ecosystem organization. This article reviews and discusses those key aspects. The gradually obtained results have contributed to a better understanding and quantification of feedback between river morphodynamics and vegetation at nested spatiotemporal scales, from plant species traits to the riverine landscape. Furthermore, the biogeomorphological approach advocated for more than 20 years now has clearly facilitated the extension of the discipline of geomorphology to ecology in general and evolutionary ecology in particular and thus contributed to a more integrative vision of earth surface processes.

Dynamic error prediction and link strain feedback control for a novel heavy load multi-DOF envelope forming machine

Thin walled and high rib components (TWHRC) are widely used for rocket cabins, aircraft wings, panels and frames in aerospace equipment as critical load bearing structures. This paper is aimed to develop a novel multi-DOF envelope forming machine (MEFM) with parallel kinematic mechanism (PKM) to realize efficient manufacturing of TWHRC with high performance. Since the heavy load MEFM with PKM has many total differences with the light load PKM, such as dynamic error and precision control, this paper conducts research on dynamic error prediction modelling and precision control method for this novel heavy load MEFM.Firstly, the configuration of MEFM is developed and the kinematic model of MEFM is established. Then, a RFC (rigid-flexible coupling) and RFE (rigid finite element) coupling dynamic model of MEFM is established considering time-variation of kinematic configuration caused by large deformation of all the kinematic elements. Based on the dynamic model, the dynamic deformation and error of MEFM are predicted and their generation mechanisms are revealed. It is found that the linear compression deformation of two-force links is the largest among all the kinematic elements, and it is the most sensitive to the heavy forming load and the most insensitive to the installation error of strain gauges, thus suiting for the feedback of large deformation of MEFM. Based on the above findings, a novel link strain feedback and dynamic error compensation control method is proposed. Finally, experiments under different control methods are conducted, showing that the motion precision of envelope tool and forming precision of formed components under the proposed control method are remarkably improved compared with that under the traditional control method (The geometric deviation of formed components under the proposed control method is remarkably reduced to −18 ∼ 32um from −64 ∼ 54um under the traditional control method). The experimental results verify that the proposed dynamic error prediction model and link strain feedback control method for heavy load MEFM are reasonable.

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review

BackgroundDuring the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation). MethodsThis review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread. FindingsOf the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared versions of their model with and without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves. InterpretationWhile many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- or overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics. FundingThis study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).

Revolutionizing eLearning Assessments: The Role of GPT in Crafting Dynamic Content and Feedback

Revolutionizing eLearning Assessments: The Role of GPT in Crafting Dynamic Content and Feedback

Feedback and Galaxy Dynamics: A Study of Turbulence and Star Formation in 34 Galaxies Using the PHANGS Survey

The correlation between interstellar turbulent speed and local star formation rate surface density, ΣSFR, is studied using CO observations in the PHANGS survey. The local velocity dispersion of molecular gas, σ, increases with ΣSFR, but the virial parameter, α vir, is about constant, suggesting the molecular gas remains self-gravitating. The correlation arises because σ depends on the molecular surface density, Σmol, and object cloud mass, M mol, with the usual molecular cloud correlations, while ΣSFR increases with both of these quantities because of a nearly constant star formation efficiency for CO. Pressure fluctuations with ΔΣSFR are also examined. Azimuthal variations of molecular pressure, ΔP mol, have a weaker correlation with ΔΣSFR than expected from the power-law correlation between the total quantities, suggesting slightly enhanced star formation rate (SFR) efficiency per molecule in spiral arms. Dynamical equilibrium pressure and SFR correlate well for the whole sample, as PDE∝ΣSFR1.3 , which is steeper than in other studies. The azimuthal fluctuations, ΔP DE(ΔΣSFR), follow the total correlation P DE(ΣSFR) closely, hinting that some of this correlation may be a precursor to star formation, rather than a reaction. Galactic dynamical processes correlate linearly such that ΣSFR∝(ΣgasR)1.0±0.3 for total gas surface density Σgas and galactic dynamical rates, R, equal to κ, A, or Ω, representing epicyclic frequency, shear rate A, and orbit rate Ω. These results suggest important roles for both feedback and galactic dynamics.

Chinese satellite frequency and orbit entity relation extraction method based on dynamic integrated learning

Given the scarcity of Satellite Frequency and Orbit (SFO) resources, it holds paramount importance to establish a comprehensive knowledge graph of SFO field (SFO-KG) and employ knowledge reasoning technology to automatically mine available SFO resources. An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations. Unfortunately, there is currently no publicly available Chinese SFO entity Relation Extraction (RE) dataset. Moreover, publicly available SFO text data contain numerous NA (representing for “No Answer”) relation category sentences that resemble other relation sentences and pose challenges in accurate classification, resulting in low recall and precision for the NA relation category in entity RE. Consequently, this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes. To address these challenges, this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning. This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data. The proposed approach combines integrated learning and pre-training models, specifically utilizing Bidirectional Encoder Representation from Transformers (BERT). In addition, it incorporates one-class classification, attention mechanisms, and dynamic feedback mechanisms to improve the performance of the RE model. Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.

Service quality improvement strategies of online car-hailing based on SPC-PCN method

Online car-hailing service is practicing the concept of sharing, bringing new energy and convenience to the transportation market. However, with its rapid development, service issues have become increasingly prominent, and improving service quality is the key to the sustainable growth of the online ride-hailing industry. Microblog, as a major online platform for gathering public views, have become one of the significant media for publishing and disseminating information nowadays, which also contains numerous views of online taxi users on online taxi services. Aiming at the deficiency of online car-hailing service under microblog public opinion, this paper integrates Service Profit Chain (SPC) and Process Chain Network (PCN) from the perspective of service management. Firstly, a crawler program is used to collect the Internet Word of Mouth data related to online taxi. Then, a BERT text classification model is constructed to roughly classify the data, and the accuracy of this model is tested to 94.54 %. Afterwards, layer segmentation is implemented based on SPC and CatBoost. Besides, a series of problems in the current online taxi service, such as poor attitude of drivers, unsafe driving, and inefficient communication with the customer service staff, have been identified by using sentiment analysis and frequent mining methods. Finally, PCN analysis is used to optimize the online taxi service, proposing strategies such as setting a reservation reminder function, building a dynamic feedback mechanism, and establishing a contact point for lost and found items. The new SPC-PCN online taxi service system constructed in this paper is vital for service quality optimization, and can also promote the sustainable growth of the online taxi market.

Piezoelectric dual network dressing with adaptive electrical stimulation for diabetic infected wound repair via antibacterial, antioxidant, anti-inflammation, and angiogenesis

Electrical stimulation therapy plays a crucial role in the healing of wounds. However, electrical stimulation based on piezoelectric materials faces limitations in dynamically matching cell activity and lacks anti-infection properties, thereby restricting its broader clinical application. Herein, a piezoelectric dual network nanofiber dressing (CuEG/PVDF) was prepared using copper/ epigallocatechin 3-gallate (Cu2+/EGCG) and polyvinylidene fluoride (PVDF). This dressing constructs dynamic force-electrical closed-loop feedback signals through cell adhesion and adaptive electrical stimulation to accelerate the healing of infectious diabetic wounds. Benefiting for the Cu2+-EGCG metal-polyphenol network, it enhances the electromechanical coupling properties, hydrophilicity, and cell adhesion of the piezoelectric double network dressing. Cell proliferation and migration were promoted by increased adhesion area, enhanced adaptive electrical stimulation, and active calcium influx. Moreover, CuEG/PVDF demonstrate significant antibacterial, antioxidant, anti-inflammatory, and angiogenic activities. In vivo experiments demonstrate that this dual-network piezoelectric dressing reduces the inflammatory response in rats, eliminates Methicillin-resistant Staphylococcus aureus (MRSA), and accelerates wound repair. Overall, this study presents a promising strategy for implementing electrical stimulation in the treatment of infected wounds.

Safety risk perception and control of water inrush during tunnel excavation in karst areas: An improved uncertain information fusion method

Water inrush disasters seriously endanger the construction safety of karst tunnels. To accurately assess the water inrush risk level of karst tunnels, actively prevent and control water inrush disasters. A risk evaluation method based on an improved cloud model (CM) and evidence reasoning (ER) combined with Monte Carlo simulation is proposed, which is applied in a water inrush risk evaluation of the Yaguan tunnel. The results are as follows. (1) This method considers the uncertainty and reliability of the water inrush risk information for karst tunnels. (2) Through an iterative evaluation with Monte Carlo simulation, the dynamic feedback mechanism of the risk state of the karst tunnel construction system is established. (3) Local sensitivity analysis is performed to identify risk factors and related measures are proposed. Hence, the research method can be used to effectively evaluate the water inrush risk level and provide references for similar project risk evaluations.