1. Introduction
In recent times, powerful earthquakes have struck cities worldwide, one after another. Since establishing the Sendai framework at the UN World Conference on Disaster Risk Reduction in 2015, the demand for resilience strengthening of structures is further attracting international researchers’ interest. This situation certainly includes buildings of various types.
Structural health monitoring fulfills an important role in the peri-disaster and post-disaster phases, respectively during and after disasters. Building collapse must be prevented even after an earthquake. A recent catastrophic event, the 2015 Gorka earthquake, caused the complete collapse of 500,000 buildings and the partial collapse of 250,000 buildings, according to the Japan International Cooperation Agency (JICA) [
1]. Another recent seismic event, the 2023 Turkey–Syria earthquake, caused more than 164,000 buildings to be destroyed or severely damaged, as reported by the Turkish Ministry. Such damage derives from a lack of proper assessment of structural capacity and a lack of rapid seismic strengthening. Findings indicate that continuous efforts at damage prevention must be pursued locally and globally.
Most buildings in countries with strict seismic design criteria, such as those of Japan, can withstand strong ground motions. Nevertheless, several buildings have been reportedly unable to maintain their functions because of damage to secondary building components [
2]. Eventually, some buildings became unsafe for use as evacuation shelters during post-disaster phases of recovery [
3]. In addition, the durations required for emergent inspections have been raised as a primary concern. In spite of enormous efforts by engineers and public servants, damage inspections after the 2016 Kumamoto earthquake took 57 days to complete [
4]. One reason underlying this long period is that the engineers able to complete on-site inspections were few at the municipality level. However, in contrast to restrictions of human resources, demands from society for the continuity of building use are skyrocketing.
Faced with these needs, structural health monitoring and damage detection are attracting interest among researchers and engineers. Generally, they are classifiable as global and local approaches [
5]. It is noteworthy that global structural health monitoring uses the following representative methods: 1) natural frequency-based methods, 2) mode shape-based methods, 3) dynamically measured flexibility matrix based methods, 4) neural network methods, and 5) generic algorithm methods [
6].
Ji et al. [
7] conducted full-scale shaking table tests as well as monitoring of building vibration. Results obtained by analyzing the shift of natural frequencies of building structures demonstrated the effectiveness of vibration-based damage diagnosis.
Okada et al. reported the application of a three-dimensional structural monitoring system for a full-scale six-story RC building [
8]. In addition, a cost efficient method was established to interpolate responses from the limited recorded data.
Moreover, Gislason et al. [
9] proposed an automated structural health monitoring system based on time history analysis. Through rigorous numerical modeling, it was demonstrated that damage can be identified with story-level precision. The degree of damage can be quantified accurately based on floor accelerations caused by ambient wind forces.
In addition, Alampalli et al. [
10] classically investigated the sensitivity of modal characteristics to damage in a laboratory-scaled bridge span. Through the comprehensive investigation, Alampalli et al. [
10] concluded that the local damage does not necessarily change mode shapes more significantly at the damage location or near damage locations than in other areas.
Structural health monitoring and damage detection at the local level are also continuing their evolution internationally. For this purpose, sensors of various kinds, such as strain gauges, accelerometers, fiber optical sensors, displacement sensors, piezoelectric sensors, and Doppler vibrometers, have been developed to realize structural health monitoring [
11]. The classical technique to detect local damage uses strain gauges. Recently, they have become widely available on the market. However, they are fragile and unsuitable for long-term monitoring. Consequently, they are commonly used for laboratory experiments.
The recent development of image sensing has realized damage detection using digital images. Earlier achievements by Chida and Takahashi [
4] enabled the detection and evaluation of quantitative damage at the ground level of timber houses using pre-post morphological processing combined with semantic segmentation by deep learning.
From a simplified perspective, Kishiki et al. [
12] attempted to visualize the residual strength of buckled steel members. The magnitude of buckling deformation was measured during cyclic loading tests. The strength and deformation magnitude were correlated. Ultimately, an evaluation equation was proposed for instant strength evaluation.
Recent efforts at structural health monitoring are being aimed at sensors of novel types, specifically piezoelectric sensors. Such sensors detect the applied force or displacement and then generate a voltage. Compared with other monitoring sensors and techniques, piezoelectric sensors provide numerous benefits such as small size, light weight, low cost, high sensitivity, and availability in various formats [
11]. By virtue of these benefits, piezoelectric sensors are applied practically for aerospace and civil engineering structures [
13].
Earlier, Harada et al. [
14] used piezoelectric sensors to detect crack propagation in steel specimens and RC beams. Conversions of the output voltage and strain are interrelated experimentally and theoretically. Furthermore, Harada et al. [
14] reported that a charge amplifier with low energy consumption took stable measurements in a static condition. Therefore, Harada et al. [
14] concluded that the piezoelectric sensor is effective, particularly for local and severe damage such as that associated with concrete crack expansion.
In terms of building applications, one of the authors enthusiastically investigated the application of piezoelectric sensors for building components. Earlier, the applicability of the sensor was studied for exposed column base connections [
15], and welded connections between a beam and column [
16] because they are prone to being damaged in strong earthquakes. The result demonstrated that the piezoelectric sensor adequately detects structural damage in the inelastic phase. Therefore, this sensor is promising for use in an inexpensive and durable health monitoring system.
Generally, buildings comprise main structural components (columns, beams, etc.) and secondary components (concrete slabs, folded roof plates, etc.). Earlier reports revealed that the secondary components function as a restraint on the primary structural members. Their restraint performance is generally represented as the spring stiffness or strength [
17,
18,
19,
20,
21,
22,
23,
24].
Steel beams are assembled with a concrete slab through shear connectors in an ordinary building with several floors (
Figure 1(a)). It is widely recognized that a concrete slab demonstrates restraint performance along the in-plane direction and out-of-plane direction. Although cracks in the concrete slab originate during cyclically applied stress from the earthquake, enhancement of the buckling strength was confirmed by experimentation [
25].
By contrast, single-story buildings (gymnasiums, warehouses, etc.) on the top floor of multiple-story buildings can only have folded roof plates (
Figure 1(b)). According to an earlier experiment, the folded roof plate demonstrates high restraint performance, but its rigidity is much lower than that of a concrete slab. Their buckling strength was derived theoretically and analytically in an earlier study [
26]. In addition, the mechanical performance of the folded roof plate was evaluated at the component level [
27].
As evaluation methods are becoming more sophisticated, as introduced above, the necessity of securing the designed restraint performance is being raised as an important concern. However, structural health monitoring and damage detection technology are usually intended for the global frame or for the primary structural components. Considering that the bracings are generally damaged before member buckling and subsequent strength deterioration, damage detection of secondary structural components is rather important.
Based on the discussion presented above, this study was conducted to detect concrete slabs and folded roof plate damage using inexpensive yet consistent and reliable measures. Specifically, this study applies piezoelectric sensors. For this purpose, this study applied cyclic loading tests to a component model of composite beam and steel frame subassembly with folded roof plates. Because the prospective damage position must be analyzed in advance, finite element analysis (FEA) is demonstrated for these assessments. The sensor output and the damage state were compared to investigate their adaptability to the damage detection of secondary building components.
Figure 1.
Installation of secondary structural components: (a) Concrete slab; (b) Folded roof plate.
Figure 1.
Installation of secondary structural components: (a) Concrete slab; (b) Folded roof plate.
Figure 2.
Component model of composite beam (unit: mm): (a) Side view; (b) Front view; (c) cross-section view.
Figure 2.
Component model of composite beam (unit: mm): (a) Side view; (b) Front view; (c) cross-section view.
Figure 4.
Finite element analysis model.
Figure 4.
Finite element analysis model.
Figure 5.
Constitutive law of concrete and separation: (a) Compression; (b) Tension; (c) Cyclic; (d) Separation.
Figure 5.
Constitutive law of concrete and separation: (a) Compression; (b) Tension; (c) Cyclic; (d) Separation.
Figure 6.
Distribution of tensile cracks: (a) Load-displacement relation; (b) d=2.1 mm; (c) d=5.0 mm; (d) d=10.0 mm.
Figure 6.
Distribution of tensile cracks: (a) Load-displacement relation; (b) d=2.1 mm; (c) d=5.0 mm; (d) d=10.0 mm.
Figure 7.
Positions of piezoelectric sensors.
Figure 7.
Positions of piezoelectric sensors.
Figure 8.
Cyclic behavior of the specimen.
Figure 8.
Cyclic behavior of the specimen.
Figure 9.
Fracture process: (a) d=-2.0 mm; (b) d=-6.0 mm; (c) d=-10.0 mm; (d) d=-14.0 mm.
Figure 9.
Fracture process: (a) d=-2.0 mm; (b) d=-6.0 mm; (c) d=-10.0 mm; (d) d=-14.0 mm.
Figure 10.
Transition of crack width and piezoelectric sensor output: (a) transition of crack width and sensor output; (b) crack and sensor positions.
Figure 10.
Transition of crack width and piezoelectric sensor output: (a) transition of crack width and sensor output; (b) crack and sensor positions.
Figure 11.
Relation between slab damage and piezoelectric sensor output: (a) crack width; (b) crack width velocity.
Figure 11.
Relation between slab damage and piezoelectric sensor output: (a) crack width; (b) crack width velocity.
Figure 12.
Loading frame: (a) Floor plan; (b) Slider detail; (c) A-A’ section view; (d) 1-1’ section view.
Figure 12.
Loading frame: (a) Floor plan; (b) Slider detail; (c) A-A’ section view; (d) 1-1’ section view.
Figure 13.
FEA model and buckling deformation: (a) FEA model; (b) buckling deformation.
Figure 13.
FEA model and buckling deformation: (a) FEA model; (b) buckling deformation.
Figure 14.
Cyclic behavior of I-shaped beam assembled with a folded roof plate.: (a) No. 1; (b) No. 2.
Figure 14.
Cyclic behavior of I-shaped beam assembled with a folded roof plate.: (a) No. 1; (b) No. 2.
Figure 15.
Procedure used to draw the skeleton curve: (a) Hysteresis curve; (b) cumulative hysteresis curve; (c) skeleton curve.
Figure 15.
Procedure used to draw the skeleton curve: (a) Hysteresis curve; (b) cumulative hysteresis curve; (c) skeleton curve.
Figure 16.
Ultimate state of the folded steel plate: (a) Deformation on the whole part; (b) Zoomed image of sensor.
Figure 16.
Ultimate state of the folded steel plate: (a) Deformation on the whole part; (b) Zoomed image of sensor.
Figure 17.
Skeleton curve and output of the piezoelectric sensor: (a) Positive (No. 1); (b) Negative (No. 1); (c) Positive (No. 2); (d) Negative (No. 2).
Figure 17.
Skeleton curve and output of the piezoelectric sensor: (a) Positive (No. 1); (b) Negative (No. 1); (c) Positive (No. 2); (d) Negative (No. 2).
Table 1.
Mix proportions.
Table 1.
Mix proportions.
W/C |
s/a |
Unit materials content [kg/m3] |
Water |
Cement |
Sand |
Gravel |
Admixture |
53.0 |
47.5 |
178 |
336 |
829 |
933 |
4.36 |
Table 2.
Material properties (concrete).
Table 2.
Material properties (concrete).
Compressive strength [N/mm2] |
Tensile strength [N/mm2] |
Modulus of elasticity [N/mm2] |
26.4 |
2.1 |
22,836 |
Table 3.
Material properties (steel plate).
Table 3.
Material properties (steel plate).
Part |
Thickness [mm] |
Yield strength [N/mm2] |
Ultimate strength [N/mm2] |
Elongation [%] |
Connector |
16 |
285 |
436 |
46 |
Web |
8 |
293 |
458 |
37 |
Flange |
12 |
257 |
440 |
43 |
Table 4.
Material properties (steel bar).
Table 4.
Material properties (steel bar).
Diameter [mm] |
Yield strength [N/mm2] |
Ultimate strength [N/mm2] |
Elongation [%] |
10 |
378 |
509 |
28 |
Table 5.
Material test results.
Table 5.
Material test results.
Part |
Specimen |
Thickness [mm] |
Yield strength [N/mm2] |
Ultimate strength [N/mm2] |
Flange |
No. 1 |
6 |
313.9 |
465.9 |
|
No. 2 |
6 |
294.8 |
443.5 |
Web |
No. 1 |
9 |
368.3 |
475.1 |
|
No. 2 |
8 |
334.1 |
456.2 |
Folded roof plate |
No. 1 |
0.5 |
347.4 |
393.1 |
|
No. 2 |
0.5 |
341.7 |
389.9 |