Topological risk mapping of runway overruns: A probabilistic approach

Trucco, Paolo; Ambroggi, Massimiliano De; Leva, Maria Chiara

doi:10.1016/j.ress.2015.06.006

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…The output showed that the accidents occurrence rate related to Take-Off and Landing does not identify a clear positive or negative trend over the period 1990-2008. While most of the previous models gave single probability values as the output, in 2015 Trucco et al proposed a methodology which contains a two-step procedure and returns probability and severity results in the form of a topological grid as output. Therefore, by superimposing this topological risk grid on the terrain surrounding the runway it would have been possible to plan the mitigation measures, reduce the probability of occurrence and the possible consequences; these, in one word, correspond to the risk of accident on the infrastructure (Trucco et al, 2015).…”

Section: Literature Review and Reference Model Selectionmentioning

confidence: 99%

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Sensitivity analysis of influencing factors in probabilistic risk assessment for airports

2018

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Risk assessment methods in aviation greatly rely on the knowledge of the factors influencing risk and safety during daily operations. One of the weak points of the common approaches in aerodromes is the qualitative method to support decisions respect to quantitative evaluations. In this study, three air-ports with diverse characteristics (i.e.; aircraft annual movements, airfield geometry, and runway features) were selected for the analysis.The RSARAÓ (Runway Safety Area Risk Assessment) software, which is based on the Aircraft Cooperative Research Program (ACRP) model, has been utilized as a starting point for further sensitivity analyses of probabilistic risk assessment of each airport's runway with determined casual factors, includ-ing runway geometry, traffic characteristics, and weather conditions. A comprehensive airports incident/accident database between years 2000 and 2015 was also used to perform the sensitivity analyses.By providing different independent variables as input in the frequency model of RSARA, the outputs were useful to determine the influence of each of the casual factors on the accident probability of occur-rence. Selected variables include: runway length in terms of declared distances, Runway Safety Area geometry, instrumental landing system category, weather operational data and annual traffic growth rate. The sensitivity analyses showed that the weather condition and runway related factors played a major role in increasing or decreasing the probability of the accident; the probability of landing overrun (LDOR) can be increased by four times, for instance, due to specific combinations of runway length and climatic conditions. Engineered arrestor beds such as EMAS also has the potential to decrease by 50% the risk of LDOR and can be selected as an effective choice compared to other pavement materials within the RSA.Identifying critical variables on the occurrence of high-severity runway accidents could influence the aerodrome design, operating scenarios, regulations, emergency planning and risk management measures and techniques.

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Section: Literature Review and Reference Model Selectionmentioning

confidence: 99%

“…Aviation performances can be divided into two categories of operation; normal operation with stochastic variations in performance and abnormal operation (Trucco et al, 2015). Measures to be employed for increasing safety could be categorized as passive or active measures.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of influencing factors in probabilistic risk assessment for airports

2018

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“…They determine the relationship of landing danger level and influence factors from on analyzing 1978 landing samples of the aircraft. Trucco et al use the distribution of the topological landing risk maps using two-step Monte Carlo method (Trucco et al , 2015). A regression model of operations and landing performance is created by analyzing 21 factors that affect landing safety from 293 recorders (Wang et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Lateral autonomous carrier-landing control with high-dimension landing risks consideration

Wang

Zhang

Zhu

et al. 2020

AEAT

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Purpose This paper aims to propose a novel model predictive control (MPC) with time varying weights to develop a lateral control law in an automatic carrier landing system (ACLS), which minimizes landing risk and improves flight quality. Design/methodology/approach First, a nonlinear mathematic model of an F/A-18 aircraft during lateral landing is established. Then the landing model is linearized in the form of state deviations on the equilibrium points. Second, landing risk windows are proposed and a high-dimensional landing risk model is addressed through a back propagation (BP) neural network. The trained samples are acquired based on a pilot behavior model. Third, time varying weights created from the lateral landing risk are introduced into the performance function of MPC. Optimal solution is solved quicker and some state deviations are focused on and eliminated. Fourth, the algebraic inequalities are substituted by the linear matrix inequalities (LMIs), which are easily calculated by the computers. Findings On a semi-physical platform, the proposed method compares with a traditional MPC algorithm and a modified MPC with an additional term. The test results indicate that the proposed algorithm brings about an excellent landing performance as well as an ability of eliminating landing risk. Practical implications The landing phase of a carrier-based aircraft is one of the most dangerous and complicated stages, and the algorithm proposed by this paper plays a vital role in the lateral landing. Originality/value This paper establishes a lateral landing risk model, which considers not only the current landing state but also the future touchdown point. This lateral landing risk is integrated into the time varying weights of the MPC algorithm so that the state deviations and landing risk can be both reduced in the rolling optimization.

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“…The map and distribution of the dynamically topological risk are acquired by utilizing the two-step Monte Carlo algorithms. 22 In particular, the previous data during landing are employed to further optimize the landing risk model. Kuchar et al 23 regard the risk as the operational constraint conditions in the decision-aiding system, which is an innovation idea.…”

Section: Introductionmentioning

confidence: 99%

Automatic flight control design considering objective and subjective risks during carrier landing

Wang

Zhang

Zhu

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a design scheme of automatic carrier landing system control law based on combination of the objective risk and the subjective risk is proposed, in order to improve the safety and flying quality of the landing. The nonlinear longitudinal mathematical model is constructed in the air wake turbulence condition during carrier landing, which is transformed into a linear perturbed model by the state-space equations with deviation state variables. The concepts of the objective risk and the subjective risk in the recovery of an aircraft aboard a carrier are addressed. A principle of predicting the future states based on the current ones is put forward so that a mathematic model for the objective risk is established, synthetically considering the current and future landing state deviations. For the other risk, the corresponding model is obtained by the subjective experiences of the pilots in the flight simulation tests. Furthermore, a novel model predictive control algorithm, which contains the additional subjective risk and the time-varying weights of the state terms, is proposed. Automatic carrier landing control law is built by introducing the objective risk, the subjective risk, and the effect of carrier air wake disturbance. In the rolling optimization progress, these time-varying weights are dynamically tuned according to the constantly changing objective risk to control the state deviations and suppress this risk, while the subjective risk is handled by the additional risk terms. Besides, the action of carrier air wake disturbance is considered and compensated in the derivation of the linear matrix inequalities. Test results based on a semi-physical simulation platform indicate that the new automatic carrier landing system control algorithm proposed by this article brings about an excellent carrier landing performance as well as an improved flying quality.

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Topological risk mapping of runway overruns: A probabilistic approach

Cited by 13 publications

References 11 publications

Sensitivity analysis of influencing factors in probabilistic risk assessment for airports

Sensitivity analysis of influencing factors in probabilistic risk assessment for airports

Lateral autonomous carrier-landing control with high-dimension landing risks consideration

Automatic flight control design considering objective and subjective risks during carrier landing

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