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Structural health assessment of prefabricated concrete elements using Acoustic Emission: Towards an optimized damage sensing tool

机译:使用声发射对预制混凝土构件进行结构健康评估:采用优化的损伤检测工具

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The design feasibility and structural integrity of prefabricated construction elements is assessed (concrete cantilever slab carrying thermal insulation break reinforced with steel/basalt rebars and ultra-high-performance concrete) by performing incremental bending tests. The developed damage mechanisms govern failure, therefore their identification appears crucial: concrete cracking, rebars-concrete interface debonding, rebars slippage, concrete crush under compression. However, the complex fracture cannot be effectively characterized by measuring the deterioration of the mechanical properties. In this study, Acoustic Emission (AE) is applied to highlight internal damage progress. It is found based on cumulative AE hits distribution under incremental load and considering the Kaiser and Felicity effects, damage initiation is identified. The damage severity is quantified using Load/Calm ratios. AE sources localization identifies the moment at which the rebars plastically deform; debond from concrete; slip and when concrete cracks and crushes under compression. The damage mechanisms are discriminated based on acoustic signal features distribution as damage evolves (specifically, average frequency and the ratio of rise time over amplitude). It is shown that AE appears an essential and accurate monitoring method that fully characterizes the damage on complex structures. The promising results open new horizons towards the design of fully autonomous prefabricated elements carrying a smart on-line AE sensing tool. (C) 2019 Elsevier Ltd. All rights reserved.
机译:通过进行增量弯曲测试,评估了预制建筑构件的设计可行性和结构完整性(带有钢/玄武岩钢筋和超高性能混凝土增强的隔热断面的混凝土悬臂楼板)。发达的破坏机制控制着失效,因此,它们的识别显得至关重要:混凝土开裂,钢筋混凝土界面脱粘,钢筋滑移,受压混凝土压碎。然而,不能通过测量机械性能的劣化来有效地表征复杂的断裂。在这项研究中,声发射(AE)用于强调内部损伤的进展。根据增量负载下的累积AE命中分布发现,并考虑Kaiser和Felicity效应,可以确定破坏的开始。使用载荷/静压比来量化损伤的严重程度。声发射源的定位确定了钢筋塑性变形的时刻。从混凝土上剥离滑移,以及在压缩下混凝土开裂和破碎时。根据损伤演变的声音信号特征分布来区分损伤机理(具体来说,是平均频率和上升时间与振幅之比)。结果表明,AE似乎是一种基本且准确的监测方法,可以充分表征复杂结构的损坏。令人鼓舞的结果为搭载智能在线AE传感工具的全自动预制构件的设计开辟了新视野。 (C)2019 Elsevier Ltd.保留所有权利。

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