自動化檢測鋸槽與養護管理策略

Automated runway groove measurement and maintenance management strategy

朱晧睿(Hao-Jui Chu)

指導教授 ： 周家蓓

國立臺灣大學/工學院/土木工程學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201902026

摘要

鋸槽在機場跑道安全中扮演著至關重要的角色，鋸槽能有效增加跑道表面的排水，使得整體跑道的抗滑值提升，進而減少飛機機輪與跑道鋪面產生水滑之現象，因此美國聯邦航空總署 (FAA)在規範 Advisory Circular AC 150_5320_12C中明訂鋸槽之幾何規範，但於過去缺乏有效率之驗收機制，直到2009年臺大研究所群提出第一套自動偵測鋸槽之方法，而至今已有諸多學者提出多樣偵測鋸槽之方法。然而鋸槽之幾何狀態，隨著鋪面表面破壞逐漸產生變形，使得鋸槽無法在程式中被有效辨識出來，造成程式中評估的鋸槽幾何成果，並非是真實的跑道面鋸槽狀況，因此機場管理單位難以在鋸槽破壞後掌握正確狀況，做出正確的決策。
因此本研究提出新的演算方法(NTUGroove)來偵測鋸槽，演算法中透過低通濾波的方式產生一參考剖面，有效的偵測出剖面中所有的低槽，再透過噪點刪除的方式剔除噪點，且區分出鋸槽與鋸縫，並提出一鋸槽理想間距的計算方式，進行下一步驟之鋸槽補償，最終得出所有鋸槽位置以及幾何數值。為評估本研究演算法之準確性，本研究以混淆矩陣分析演算法(NTUGroove)的辨識成果，在辨識鋸槽上，演算法準確度為Precision=0.99、Re-call=0.99；在辨識鋸縫上，演算法準確度為Precision=0.95、Re-call=0.95，皆屬於高精確度之成果。而為使該演算法能被善用於鋸槽之管理分析，本研究透過以上所建立之演算法(NTUGroove)提出追蹤管理鋸槽上可應用之方法，包含利用全剖面鋸槽幾何的平均數值，判斷通過率數值的高低，以及提出一鋸槽排水能力之計算方式，再輔以滾動分析去找尋跑道上最佳的鋸槽養護位置。綜合以上研究之成果，機場管理單位未來能透過該研究之演算法與分析方式，對於機場鋸槽長期管理與決策上有更信賴之參考數值，進而有效提升整體跑道之安全性。

關鍵字

鋸槽 ； 低通濾波 ； 鋸槽補償 ； 混淆矩陣 ； 維護管理方式 ； 鋸槽排水能力 ； 滾動分析

並列摘要

The groove plays a vital role in airport runway safety, grooves can effectively increase the drainage of the runway surface, so that the skid resistance value of the overall runway is increased, thereby reducing hydroplaning. Federal Aviation Administration (FAA) specifies the geometry of the groove in the Advisory Circular AC 150_5320_12C, but there was not efficient acceptance mechanism in the past; until 2009, the research team of National Taiwan University proposed the first method to automatically detect the groove; after that, many teams have proposed various methods for detecting the groove. However, the geometry of the grooves are gradually deformed as the surface of the pavement is broken, so that the groove cannot be effectively recognized in the program easily. It made the result of the groove geometry evaluated in the program was not the actual groove condition. Therefore, it is difficult for the airport management agency to control the correct situation of the groove and make the correct decision after grooves are destroyed.
This research proposed a new algorithm (NTUGroove) to detect the groove. In the algorithm, a reference profile is generated by low-pass filtering, which effectively detects all the low dips in the profile, and then removes the noise by noise elimination. After that, algorithm distinguishes the dips between the grooves and the joints, and then proposes a calculation method of the ideal spacing of the groove, helping to the groove compensation in the next step, and finally obtain the position and geometric value of all the grooves. In order to evaluate the accuracy of the algorithm, this research used the confusion matrix to analysis the results of the algorithm (NTUGroove). On the identification of the grooves, the average accuracies of the algorithm are Precision=0.99 and Re-call=0.99; on the identification of the joints, the accuracies of the algorithm are Precision=0.95 and Re-call=0.95; all of the accuracies are the high precision. To make this algorithm be useful for the management, this research proposed methods of selecting the location for the groove maintenance based on the algorithm (NTUGroove) established above; including using the geometric results of grooves to determine the proportion of passing specification, calculating the drainage capacity of every grooves, and applying the rolling analysis to find the best position for maintenance grooves on the runway.
All in all, this research makes the airport management agency can use the algorithm(NTUGroove) and analysis method to get a more reliable reference value for the long-term management and decision-making; finally effectively improving the safety of the overall runway.

並列關鍵字

groove ； low-pass filtering ； groove compensation ； confusion matrix ； groove maintenance ； groove drainage capacity ； rolling analysis

參考文獻

[1] Beaton, John L., Ernest Zube, and John Skog. "Reduction of Accidents by Pavement Grooving." Special Rep 101 (1969): 110-125.

[2] Butterworth, Stephen. "On the theory of filter amplifiers." Wireless Engineer 7.6 (1930): 536-541.

[3] Cai, A. Zhixing, et al. "Automated groove identification and measurement using long short-term memory unit." Measurement 141 (2019): 152-161.

[4] Circular, FAA Advisory. "150/5320-12C/Appendix 3/Measurement." Construction and Maintenance of Skid Resistant Airport Pavement Surfaces 997 (1997).

[5] Chou, Chia-Pei, Hernan Romero, and Ai-Chin Chen. Algorithms Comparison of Wheelchair Pathway Serviceability Evaluation. No. 19-04653. (2019).