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Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

As an example, many of the bridges in Japan were constructed during the years of rapid economic growth. It is said that the number of bridges (2 m or longer) exceeding 50 years of age will increase in the coming years so that bridges 50 years or older will exceed 65% of all bridges in the country in 20 years [

Under these circumstances, growing attention is being paid to the development of the method of detecting bridge damage by evaluating the structural response of the bridge measured when a vehicle equipped with a sensor passes over it [

of an aged bridge declines sharply, to the “deterioration” period by continually monitoring the bridge of interest (see

In order to solve the remaining problems mentioned above, a long-term field test of the system has been conducted over a period of about four years by using an in-service bus operating in the city of Ube, Yamaguchi Prefecture, Japan and real bridges located on the bus route. This paper described the validation results obtained from the long-term monitoring and discusses the usefulness of the system. Problems of the conventional observation method based on “characteristic deflection”, which is a bridge condition indicator that makes possible efficient detection of structural anomalies of the bridge being monitored, are identified, and a new observation method that enhances the damage detection sensitivity of the system is evaluated. This study also examines the influence of artificial damage (guardrail removal) on “characteristic deflection” to evaluate the sensitivity of the system in detecting damage given to the field test bridge. Finally, various study results as mentioned above are put together to systematically discuss the practical scope of application, damage detection accuracy and remaining problems of the system.

In this chapter, the details of theoretical background and the system are described in detail the flow of the bridge monitoring process (i.e. bus monitoring system) that uses an in-service fixed-route bus as mentioned in the previous chapter. Advantages and principle of the bridge structure anomaly detection (condition assessment) method using the under-spring vibration of an in-service bus (city bus), which has been largely established as a result of the authors’ studies, are described in detail. This chapter also describes the procedure for calculating “characteristic deflection,” which is an indicator used to evaluate the degree of deterioration (bridge condition) of short and medium span bridges.

The aim of the bus monitoring system is to detect anomalies (deterioration) of the bridge of interest by using vibration data, mainly vertical acceleration data, obtained from the acceleration sensor installed under the rear wheel spring of an in-service fixed-route bus.

Main reasons for having decided to use an in-service fixed-route bus (i.e. a heavy vehicle) are as follows:

a) If a large vehicle about 10 m long (span) is used for measurement, it is highly likely that when the vehicle crosses a short and medium span bridge that is the only vehicle in the same lane on the bridge.

b) If a short and medium span bridge, which is has relatively high flexural stiffness, is to be vibrated, it is necessary to use a relatively heavy vehicle.

c) If a fixed-route bus is used as a source of bridge excitation, it is easy to reproduce measuring conditions such as the time of passage, route, frequency and velocity.

d) Since a fixed-route bus equipped with a sensor makes the rounds, it is possible to monitor main short and medium span bridges in a particular area on a regular basis. As a result, substantial cost reduction can be achieved because there is no need to install sensors to all bridges to be monitored.

e) The electric power for the measuring instruments used can be supplied by the power supply of the bus.

With regard to the first item, vehicles moving in the opposite direction, or oncoming vehicles, are regarded as an external disturbance factor included in operational conditions in this study (described later). The bus monitoring system is a rational system capable, by using local fixed-route buses, of monitoring bridges on a daily basis while serving as part of transport infrastructure. The bus monitoring system, however, does not identify local deteriorations and their causes because the purpose of the system is to detect damage (anomalies) indicating an overall structural problem of a bridge.

This section describes in detail the principle of operation of the bus monitoring system: how bridge anomalies are detected from vehicle vibration as proposed in a preceding study [

The case in which a vehicle crosses a bridge can be represented by a dynamic interaction between the equation of motion expressed by Equation (1) and the equation of motion expressed by Equation (2). Thus, structural models of the bridge and the vehicle are formulated with different equations of motion, and interactions at points of connection between them are expressed by input and output vectors. This approach is called the “substructure method” [

where,

To express the interaction between the bridge and the vehicle, the under- spring reaction of the vehicle is input to the bridge side as load vector,

The first step is to consider the case where various physical parameters of the bridge system and the vehicle system and the road surface roughness,

The next step is to consider the case where various physical parameters for the vehicle system and the road surface roughness,

Thus, structural anomalies of the bridge due to deterioration, etc. emerge as changes in vehicle system nodal response,

In the case of the proposed system, detection becomes easier as _{A} tends to be greater than_{s} tends to be smaller(light) than

Let us consider the upper body/lower body/bridge substructuring scheme as shown in

where, M, C and K are lumped mass, damping and stiffness matrices for a given system; and

Let differential operator D and shift operator Z be expressed as,

Then, the equation of motion in Equation (3) can be rewritten as,

Taylor expansion of Equation (5) gives,

Using Equation (4), we can obtain

If Newmark’s β method (β = 1/4) is used, the differential operator relation can be assumed as follows:

Substituting this in Equation (6) gives Equation (10):

Let k represent a post-discretization step at time t, and k + 1, the next step. Then, Equation (5) can be rewritten as

The right-hand side and the second and third terms of the left-hand side of Equation (11) are known when solving the equation at step k (=time t). Let

where,

Next, let us consider the vibration of the wheel-bridge system due to the force transmitted from the upper part of the vehicle.

As in the case mentioned earlier, the response of the wheel-bridge system to the input from the upper part of the vehicle is distributed proportionately depending on physical constants of the system. It can be inferred, therefore, that if

This section describes the concept of the method of extracting damage and deterioration related information from the vertical under-spring vibration of the bus without being affected by the dynamic characteristics of the bridge and the vehicle and road surface roughness. The vertical under-spring vibration response,

If road surface roughness is assumed to be a stationary random Gaussian process with a mean value of 0 and if dynamic displacement including the bridge-vehicle interaction is assumed to be an ergodic process and therefore Fourier-expandable, the dynamic displacement,

where,

As the next step, a total of k samples are taken from measured values of

Since the distribution of sample means should be normal according to the central limit theorem,

If sampling from

This expresses the average vertical under-spring displacement of a bus crossing a bridge. This can be rewritten, on the basis of Equations (14) and (16), as,

This means that the average of sample values obtainable from a sufficiently large number (N) of measured values of vertical under-spring displacement of a bus crossing a bridge can be extracted as values

Means of deflection,

If the law of similarity mentioned earlier holds true with respect to

Let

And it can be defined as a parameter for structural anomaly detection. After setting the value of

and its characteristics and actions to be taken are considered by using a real in-service fixed-route bus.

Step 1: Extract data on vertical acceleration during bridge crossing

Data on vertical acceleration during bridge crossing are extracted from acceleration sensor measurement data by referring to a combination of other data such as the time at which the buses crossed each bridge and GPS data.

Step 2: Estimate the time at which the midspan point was passed

Extracted data on acceleration during bridge crossing include considerable vibrations recorded at joints. It is therefore necessary to use midspan acceleration data that do not include such joint vibrations. The time at which the midspan point of a girder was passed can be estimated by identifying bridge sections meeting such criteria as duration and wave count and extracting relevant data from non-joint data. It may be difficult, however, to identify joint locations

because acceleration data may vary in magnitude depending on such factors as bus operating conditions. For accurate estimation of the time at which the midspan point was passed, therefore, attention is paid to estimated deflection diagrams obtained by integrating vertical acceleration data twice. As an example,

Step 3: Extract data on vertical acceleration during bridge crossing

Extract the midspan vertical acceleration data identified at Step 2. The most important thing in “characteristic deflection” calculation is to determine the extraction range according to such details as wave count and duration and extract acceleration waveform data from the same segment in every time. Step 3 is described in detail in the next section.

Step 4: Integrate the extracted acceleration data twice

The extracted acceleration data is converted to velocity data by integrating once and to displacement data by integrating twice. In this study, the vertical displacement obtained by integrating the vertical acceleration twice is regarded as estimated midspan deflection during bridge crossing.

of estimated deflection in this way. As shown in

Step 5: Average estimated deflections

The estimated midspan deflections during bridge crossing shown in the graph are time-averaged to calculate the “characteristic deflection” (see

Described above is the procedure for calculating the “characteristic deflection” used as an indicator in the proposed evaluation method. “Characteristic deflections” thus calculated include the effects of external disturbance factors as shown in Equation (24). It has been confirmed that “characteristic deflections” calculated as described above are significantly affected by human errors (individual errors). Efforts need to be made, therefore, to minimize human error in the calculation process.

This chapter describes on the long-term field test of the bus monitoring system for short and medium span bridges located on the municipal bus routes in the city of Ube, Yamaguchi Prefecture, Japan conducted over a period of about four years from December 2010 to September 2014. Since the field test has been conducted for about four years, a considerable amount of measurement data has been accumulated. The data thus accumulated were utilized to evaluate the influence of fixed-bus operating conditions (external disturbance factors such as weather, the number of oncoming vehicles, the number of persons in the vehicle and vehicle speed) on characteristic deflection. In addition to the derivation of conversion (correction) factors based on the correlations between various bus operating conditions (external disturbance factors) and characteristic deflection carried out in previous studies [

In order to develop and put to practical use a bus monitoring system for short and medium span bridges located on bus routes, it is necessary to conduct a series of studies involving a long-term field test using an in-service fixed-route bus. In this study, with the cooperation of Ube-city’s Transportation Bureau (UTB), long-term monitoring of short and medium span bridges located on the city’s in-service bus routes has been continued. The study focuses mainly on the following:

(1) The number of short and medium span bridges existing on the city’s bus routes and the total number of existing bridges in need of maintenance.

(2) The method of calculating “characteristic deflection,” which is an indicator of the structural health of bridges based on long-term measurement data and its usefulness in damage detection.

(3) Proposing a method for long-term observation of characteristic deflection and enhancing damage detection sensitivity.

(4) Verifying the damage detection sensitivity of characteristic deflection by use of artificial damage and setting “critical characteristic deflection” (criterion value) by use of an analysis model.

(5) Evaluation of the influence of bus operating conditions (external disturbance factors) on characteristic deflection based on long-term measurement data and an attempt at deriving conversion (correction) factors.

older than 50 years as of fiscal year 2011 is 65, which is about 15% of all bridges. In 2031 (20 years later), it will increase to 323 bridges (about 74% of all bridges), indicating a rapid deterioration of the bridges in the city. Of such short and medium span bridges located on the bus routes in Ube-city, three bridges that are thought to have deteriorated considerably, namely, “Shiratsuchi Daini Bridge (2-span RC T-girder bridge)”, “Jase Bridge (5-span PC slab bridge)” and “Shingondai Bridge (single-span prestressed concrete girder bridge built by the Bi- Prestressing Method)”, were selected for the long-term monitoring. Specifications and general views of these three bridges are shown in

This section briefly describes the bus (vehicle) used for the long-term field test. The long-term measurement using Ube-city’s municipal bus routes has been continued by using an in-service fixed-route bus (i.e. a bus actually used to transport passengers) owned by Ube-city’s Transportation Bureau. By using the three-axis acceleration sensor installed under the rear wheel spring of this

Bridge name | Completed in | Type of superstructure | Span length (m) | Bridge length (m) | ||
---|---|---|---|---|---|---|

Jase Bridge | 1976 | Span No. | Start point side 1 | Prestressed concrete slab bridge (pretensioned slab) | 18.0 | 85.0 |

2 | Prestressed concrete slab bridge (pretensioned slab) | 16.0 | ||||

3 | Prestressed concrete slab bridge (pretensioned slab) | 18.0 | ||||

4 | Prestressed concrete slab bridge (pretensioned slab) | 14.0 | ||||

End point side 5 | Prestressed concrete slab bridge (pretensioned slab) | 19.0 | ||||

Shiratsuchi Daini Bridge | 1933 (estimated) | Span No. | Start point side 1 | Reinforced concrete (T-girder) | 7.0 | 15.0 |

End point side 2 | Reinforced concrete (T-girder) | 7.0 | ||||

Shingondai Bridge | June 1998 | Single-span prestressed concrete girder bridge (Bi-prestressing method) | 22.4 | 23.6 |

vehicle, the vibration properties of the bridge being crossed by the bus were extracted as the acceleration response during bridge crossing and deflection was estimated [

Item | Specifications |
---|---|

Net vehicle weight | 8130 kg |

Gross vehicle weight | 11,485 kg |

Front axle weight | 2730 kg |

Rear axle weight | 5400 kg |

Wheel base | 4.4 m |

Name: Fuji Ceramics SA11ZSC-TI (Three-axis piezoelectric acceleration transducer with built-in amplifier) | |
---|---|

Charge sensitivity | 1 mV/m/s^{2} |

Frequency range | 1 - 8000 Hz |

Resonant frequency | 35 kHz or higher |

Maximum measurable acceleration | 4000 m/s^{2} |

Maximum allowable acceleration | 30,000 m/s^{2} or higher |

Power supply for built-in amplifier | 21 - 24 V/0.5 - 10 mA |

Temperature range | −50 − +110˚C |

Dimensions | 14.2 × 14.2 × 14.2 mm |

Mass | Approx. 11.1 g |

in Ube-city, attempts were made to systematically evaluate the influence of bus operating conditions (weather, the number of oncoming vehicles, the number of persons on the vehicle and vehicle speed), besides the acceleration response recorded with the three-axis acceleration sensor, on characteristic deflection and elucidate and quantify their correlations. During the data measurement, a two- person measuring team rode on the bus. One of them, who sat on a front seat near the bus driver, recorded details such as vehicle speed, the number of oncoming vehicles (if any) and weather conditions. The other person, who sat on a rear end seat, operated and checked on the measuring equipment and recorded the number of persons on the bus and the time at which the bus crossed the bridge in time series while collecting other information on possible external disturbance factors.

Before reporting the results of the long-term field test conducted over a period of about four years, this section touches on some fundamental findings from previous studies. First, a study was conducted to determine whether it is possible to extract the “estimated deflection” (basic data for the calculation of “characteristic deflection”) of the bridge of interest from the rear wheel under-spring ac- celeration response of a bus (vehicle). In that study, an acceleration sensor was installed in the midspan zone of the Shingondai Bridge (prestressed concrete girder bridge built by the Bi-Prestressing Method), which is one of the three

bridges selected for the present study, and the acceleration response of the bridge and the under-spring acceleration response of the bus were compared in time series. Next, another study was conducted to evaluate the influence of bus operating conditions during bridge crossing on characteristic deflection and use the findings for conversion (correction) factor derivation in future. In the study, coefficients of correlation between those conditions and characteristic deflection were derived. Although the goal of conversion (correction) by use of correlation coefficients was not achieved because the required amount of data was not available, the study succeeded in showing that the variability of characteristic deflection can be reduced by applying the moving average method to a time series. A vehicle-induced vibration simulation taking account of the coupling with the bus and the bridge, etc. was also performed by using the substructure method [

As a basic check, it is necessary to determine whether it is possible to detect damage from the under-rear-wheel-spring acceleration response of the bus when a serious structural anomaly of a bridge has occurred. In other words, it is necessary to check whether the under-rear-wheel-spring response and the bridge response are coupled. This section looks at the correlation in terms of vibration properties during bridge crossing by using data obtained from another acceleration sensor installed to the “Shingondai Bridge” mentioned earlier.

Weather: rain,

Vehicle speed: 35 km/h,

Number of oncoming vehicles: 1,

Total number of persons on the bus (including the bus driver): 10.

“Characteristic deflection” is affected by various external disturbances such as the bus operating conditions mentioned earlier. Consequently, “characteristic deflection” is inevitably subject to variation. An attempt was made, therefore, to determine changes over time in “characteristic deflection” obtained from the bus monitoring system by applying the moving average method, assuming that as the number of samples, N, increases, variations due to external disturbances such as bus operating conditions converge to a single value according to the central limit theorem. The moving average method is the method of calculating the average of data in data section (segment; the number of data sets to be averaged) by calculating averages for incrementally shifted subsections. In the previous studies, the simple moving average method, which is one of the commonly used moving average methods, was used to process characteristic deflection data. As an example,

As shown in

In the previous studies, characteristic deflection corresponding to the state of bridge damage determined in a vehicle-induced vibration simulation performed by the substructure method, a finite element method, was calculated. The intent was to develop serious deterioration (damage) criteria by which to identify the

degree of change (increase) in characteristic deflection that indicates the occurrence of serious deterioration (damage) of the bridge of interest. In this study, in view of the fact that the bridge under consideration is a prestressed concrete girder bridge (“Shingondai Bridge”, a single-span bridge built by the Bi-Pre- stressing Method) as mentioned earlier, attention is paid to the decrease in prestressing force as a kind of bridge damage. The National Institute for Land and Infrastructure Management (NILIM) of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) conducted a study on the relationship between the amount of prestress introduced and displacement (deflection) [

Structural soundness of bridge | Decrease in prestressing force | Ratio of geometrical moment of inertia relative to 0% reduction | Ratio of characteristic deflection relative to 0% reduction |
---|---|---|---|

Sound | 0% | 1.0 | 1.0 |

Deterioration Phase 1 | 50% | 0.52 | 1.93 |

Deterioration Phase 2 | 90% | 0.35 | 2.86 |

measured continually over a long period of time, and when one of those criterion values is reached, that is deemed to indicate the occurrence of some kind of serious damage in the bridge of interest, and a warning is issued so that necessary actions such as detailed inspection can be taken immediately. Actions such as detailed inspection need to be taken immediately when “characteristic deflection” has reached a criterion level on the out-bound or in-bound or in-bound route of the bus.

As of this writing, the observation of the “characteristic deflection” of the “Shingondai Bridge” is underway while comparing the amount of decrease in prestressing force with the serious deterioration (damage) criteria. This approach, however, is not applicable to bridges of other types. For those bridges, it is necessary to identify a number of types of serious damage, taking account of such factors as the characteristics and material used of each bridge, and set serious damage criteria accordingly.

On the basis of the basic findings of the previous studies mentioned in the preceding section, more measurement data have been accumulated (big data) by using the in-service municipal bus network of Ube-city, Japan. This section presents comprehensive verification results based on the monitoring data thus accumulated over a period of about four years. For that purpose, the influence of bus operating conditions (external disturbances) such as vehicle speed and the number of oncoming vehicles on changes in “characteristic deflection” (indicator used for damage detection) induced by in-service fixed-route bus operation is determined, and their correlations are reflected in conversion (correction) factors. All data on the three bridges that have been monitored by the “characteristic deflection” observation method proposed in the previous study accumulated over the four years are put together and examined to evaluate the usefulness of the proposed approach in detecting serious deterioration (damage) of the bridges being monitored.

The bus monitoring system utilizes an in-service fixed-route bus. Its operating conditions, therefore, act as external disturbances during the long-term observation of “characteristic deflection”. Because of this, as mentioned in Section 3.2.2, the amount of data accumulated in connection with the previous studies was not large enough to identify clear correlations between the bus operating conditions and “characteristic deflection”. “Characteristic deflection” also varied considerably depending on the bridge concerned and the direction of vehicle movement. Consequently, the results obtained did not make it possible to reflect their correlations in conversion (correction) factors. An attempt is being made, therefore, to reduce the influence of external disturbance factors such as bus operating conditions by applying the simple moving average method. In this section, however, with the aim of evaluating the possibility of insufficiency of accumulated data, correlations between “characteristic deflection” and the bus operating conditions (external disturbances) are re-examined and discussed by putting together the four-year monitoring data again.

In this study, the coefficients of correlation between the “characteristic deflections” of the three bridges listed in

As shown in

In view of these results, it can be concluded that although it can be shown that each of the bus operating conditions (external disturbances) somehow influences the characteristic deflection, at present it is still not possible to quantify such influence so that it can be reflected in conversion (correction) factors. Therefore, as a method of handling variations due to various external disturbance factors including bus operating conditions, the moving average method (simple moving average method) mentioned in Section 3.2.2 was applied for the purpose of observing changes over time.

On the basis of the study results described in the preceding sections, this section deals with the calculated values of “characteristic deflection” based on the long-term monitoring of the three bridges in Ube-city’s municipal bus network continued over a period of about four years, and the results of observation of changes over time in the characteristic deflection.

Bridge name | Speed (km/h) | Rainfall (mm) | Temperature (˚C) | Oncoming traffic (vehicles) | Number of persons on vehicle (persons) |
---|---|---|---|---|---|

Shingondai Bridge | 40 - 50 | 0 | 20 - 30 | 0 | 5 - 15 |

Shiratsuchi Daini Bridge | 40 - 50 | 0 | 20 - 30 | 0 | 5 - 15 |

Jase Bridge | 45 - 55 | 0 | 20 - 30 | 0 - 1 | No restriction |

Bridge name | Direction of movement | Span | Speed | Rainfall | Temperature |
---|---|---|---|---|---|

Shingondai Bridge | Toko → Nishi | − | −0.162 | 0.240 | 0.135 |

Little correlation | Weak positive correlation | Little correlation | |||

Nishi → Toko | − | −0.257 | 0.151 | −0.337 | |

Weak negative correlation | Little correlation | Weak negative correlation | |||

Shiratsuchi Daini Bridge | Nishi → Yoshi | A | −0.014 | −0.095 | 0.005 |

Little correlation | Little correlation | Little correlation | |||

B | 0.022 | −0.201 | −0.182 | ||

Weak positive correlation | Weak negative correlation | Little correlation | |||

Yoshi → Nishi | B | −0.434 | 0.317 | −0.507 | |

Negative correlation | Weak positive correlation | Negative correlation | |||

A | −0.058 | −0.008 | −0.136 | ||

Little correlation | Little correlation | Little correlation | |||

Jase Bridge | Sho → Kin | A | −0.192 | 0.091 | 0.004 |

Little correlation | Little correlation | Little correlation | |||

B | 0.026 | 0.117 | 0.071 | ||

Little correlation | Little correlation | Little correlation | |||

C | 0.087 | −0.005 | −0.044 | ||

Little correlation | Little correlation | Little correlation | |||

D | 0.159 | 0.124 | 0.206 | ||

Little correlation | Little correlation | Weak positive correlation | |||

E | 0.095 | 0.062 | 0.186 | ||

Little correlation | Little correlation | Little correlation | |||

Kin → Sho | E | −0.270 | −0.117 | −0.135 | |

Weak negative correlation | Little correlation | Little correlation | |||

D | 0.161 | −0.234 | 0.088 | ||

Little correlation | Weak negative correlation | Little correlation | |||

C | −0.348 | 0.046 | −0.144 | ||

Weak negative correlation | Little correlation | Little correlation | |||

B | −0.332 | 0.053 | 0.309 | ||

Weak positive correlation | Little correlation | Weak positive correlation | |||

A | −0.328 | 0.156 | 0.052 | ||

Weak negative correlation | Little correlation | Little correlation |

Bridge name | Direction of movement | Span | Oncoming traffic | Number of persons on bus |
---|---|---|---|---|

Shingondai Bridge | Toko → Nishi | − | −0.059 | 0.205 |

Little correlation | Weak positive correlation | |||

Nishi → Toko | − | −0.150 | −0.101 | |

Little correlation | Little correlation | |||

Shiratsuchi Daini Bridge | Nishi → Yoshi | A | 0.124 | −0.097 |

Little correlation | Little correlation | |||

B | 0.222 | 0.044 | ||

Weak positive correlation | Little correlation | |||

Yoshi → Nishi | B | −0.217 | −0.152 | |

Weak negative correlation | Little correlation | |||

A | 0.148 | −0.011 | ||

Little correlation | Little correlation | |||

Jase Bridge | Sho → Kin | A | 0.099 | 0.263 |

Little correlation | Weak positive correlation | |||

B | −0.266 | −0.270 | ||

Weak negative correlation | Weak negative correlation | |||

C | 0.024 | 0.018 | ||

Little correlation | Little correlation | |||

D | 0.328 | 0.081 | ||

Weak positive correlation | Little correlation | |||

E | 0.308 | 0.031 | ||

Weak positive correlation | Little correlation | |||

Kin → Sho | E | 0.013 | −0.044 | |

Little correlation | Little correlation | |||

D | 0.088 | 0.469 | ||

Little correlation | Positive correlation | |||

C | 0.036 | −0.322 | ||

Little correlation | Weak negative correlation | |||

B | −0.009 | 0.366 | ||

Little correlation | Positive correlation | |||

A | −0.184 | −0.030 | ||

Little correlation | Little correlation |

Coefficient of correlation | Correlation |
---|---|

0.0 - ±0.2 | Little correlation |

±0.2 - ±0.4 | Weak correlation |

±0.4 - ±0.7 | Correlated |

±0.7 - ±0.9 | Strong correlation |

±0.9 - ±1.0 | Very strong correlation |

Bridge name | Direction of movement | Number of measurement data sets |
---|---|---|

Shingondai Bridge | Toko → Nishi | 80 sets |

Nishi → Toko | ||

Shiratsuchi Daini Bridge | Nishi → Yoshi | 77 sets |

Yoshi → Nishi | ||

Jase Bridge | Sho → Kin | 66 sets |

Kin → Sho | 64 sets |

Bridge name | Direction of movement | Span | Characteristic deflection (mm) | |
---|---|---|---|---|

Average | Standard deviation | |||

Shingondai Bridge | Toko → Nishi | − | −5.218 | 1.733 |

Nishi → Toko | − | −2.909 | 1.231 | |

Shiratsuchi Daini Bridge | Nishi → Yoshi | A | −2.731 | 1.071 |

B | −2.030 | 0.868 | ||

Yoshi → Nishi | B | −1.577 | 0.727 | |

A | −2.439 | 1.021 | ||

Jase Bridge | Sho → Kin | A | −2.153 | 0.608 |

B | −1.910 | 0.533 | ||

C | −2.017 | o.651 | ||

D | −2.085 | 0.657 | ||

E | −2.467 | 0.669 | ||

Kin → Sho | E | −1.423 | 0.628 | |

D | −1.499 | 0.651 | ||

C | −1.131 | 0.547 | ||

B | −1.164 | 0.579 | ||

A | −1.532 | 0.554 |

shows the span-by-span averages and standard deviations of “characteristic deflection” and other related data for the three bridges obtained by processing the four-year data in an integrated manner.

As shown in

Thinking of bus operating conditions that may affect “characteristic deflection,” which is an indicator of the structural health of bridges, as external disturbance factors, the authors tried to quantify the correlations between the bus operating conditions and the characteristic deflection by adding new measurement data to the available data. Although certain degrees of influence of external disturbance factors (bus operating conditions) can be seen, it is as yet not possible to quantify such influence in the absence of a clear tendency or a strong correlation. As a result, it was concluded that at present it is not possible to reflect their correlations in conversion (correction) factors applicable to the bus operating conditions. It was therefore thought that the simple moving average method mentioned in Section 3.2.2 would be useful in treating the influences of the external disturbance factors on the characteristic deflection as variances.

As mentioned in the preceding chapter, “characteristic deflection” measured with the bus monitoring system is affected by various external disturbance factors such as the operating conditions of the bus. Consequently, measured values vary significantly. In order to observe long-term changes, therefore, it has been proposed that measured values be processed by the moving average method [

The moving average method is a method of analyzing data by smoothing out fluctuations of long-term time series data. The method, therefore, is widely used in various fields including engineering, finance and physical distribution. There are three widely used methods: the simple moving average method, the weighted moving average method and the exponential moving average method. Characteristics of the three moving average methods are described below.

The simple moving average (SMA) method is the method of arithmetically averaging a subset of N input data items without weighting. A simple moving average can be calculated as follows:

where, SMA_{M} is a simple moving average; N, the number of input data items; and p, the input value at each point in time.

An advantage of using Equation (25) is that if, for example, the simple moving average is to be calculated by using the input data obtained from the measurement carried out on the next day, calculation can be done by adding new input data and excluding the oldest input data as shown in Equation (26). In this method, therefore, there is no need to recalculate the sum.

This moving average method is the method used for the long-term observation of “characteristic deflection” in the previous studies [

In order to detect serious deterioration (damage) of the bridge of interest in using the bus monitoring system for bridge observation, it is necessary to detect points of abrupt change in “characteristic deflection” as soon as possible. In the conventional observation technique using the simple moving average method, however, the appearance of the influence of newly input data on characteristic deflection tends to lag behind because of averaging. This means that the detection of a point of abrupt change tends to be delayed (become less sensitive). It was therefore thought that weighting needs to be used in data processing so that detection sensitivity to newly input data can be improved.

Possible solutions to the abovementioned problem of damage detection sensitivity associated with the simple moving average method include the weighted moving average (WMA) method and the exponential moving average (EMA) method. Both of these methods assign weights to input data, but there are differences in the weighting scheme used and other details. Each method is described below.

a) Weighted moving average method

In the weighted moving average method, averages are calculated by assigning different weights to input values. To be more specific, the weighted moving average in the case where the number of data items is N is calculated by assigning weight N to the newest input value and weights N − 1, N − 2, ∙∙∙ to the next newest values so that older values have smaller weights. The formula for calculating the weighted moving average is as follows:

where, WMA_{M} is the weighted moving average; N, the number of input data items and weight; and p, input data at each point in time.

From Equation (27), we obtain

From Equation (27) and Equation (28), the difference between the numerators of WMA_{M}_{+1} and WMA_{M} can be expressed as,

For example, let Total_{M} represent the sum of the input values entered during a period of N days. Then, Total_{M} can be calculated as,

From Equation (29) and Equation (30)

Hence,

It can be seen from Equation (32) derived in the last step that as with simple moving averages, there is no need to recalculate the sum when calculating the weighted moving average reflecting the measurement data entered on the next day.

b) Exponential moving average method

Like the weighted moving average method, the exponential moving average method is a moving average calculation method that assigns different weights to individual input values. The difference is that in the exponential moving average method, the largest weight is assigned to the newest input value, but weights assigned to older input values are reduced exponentially. By so doing, greater importance is attached to newer input data, but older input data are not discarded altogether. In other words, the weight assigned to the oldest input value is not zero.

The degree of weight reduction is determined by the smoothing coefficient,

number of input data items, N, as

exponential moving average is as follows:

where, EMA_{M} is the exponential moving average;

Expanding EMA_{M}_{−1} in Equation (33) gives,

As shown in Equation (34), weights assigned to input values decrease exponentially from the weight assigned to the newest input value. In Equation (34), which expresses a sum, the value (1 −

Expanding Equation (33) by using

Equation (35) seems to indicate that only newly input data are heavily weighted and the other data are given the same weight. The fact, however, is that when a new measured value is input, it is incorporated into the previous average, and this process is repeated so that the weight assigned to the newly input value gradually decreases.

By using the three moving average methods mentioned above, this section performs sensitivity analyses of the cases where abnormal values obtained from long-term observation of changes in “characteristic deflection” are input. Focusing on the cases where abnormal values of change in the “characteristic deflection” of the “Shingondai Bridge”, one of the three bridges under consideration in this study, designed to simulate serious damage are input consecutively, the analyses compare and evaluate the following: (1) the number of inputs needed to reach the serious deterioration (damage) criterion level (“Deterioration Phase 1” defined in Section 3.2.3) and (2) the slope of the curve showing the average values obtained by each moving average method when values indicative of an anomaly are input consecutively. On the basis of the results thus obtained, an attempt is made to enhance the “characteristic deflection” observation accuracy and damage detection sensitivity of the bus monitoring system in monitoring short and medium span bridges over a long period of time.

As mentioned in Section 3.2.3, serious deterioration (damage) criteria based on the amount of decrease in prestressing force were set for the evaluation of the “characteristic deflection” of the “Shingondai Bridge” (prestressed concrete girder bridge). In this section, detection sensitivity attainable by the different moving average methods is compared in terms of the number of inputs needed to reach the Deterioration Phase 1 level in the cases where anomaly data attributable to serious deterioration (damage) are input consecutively. The analyses consider a total of four cases: the cases where constant values equal to three, four and five times, respectively, the average value of “characteristic deflection” calculated through the bus-based monitoring of the present condition of the “Shingondai Bridge” are input consecutively and the case where the input value is gradually increased to two, three, four, ...n times the average value of “characteristic deflection” (to simulate the case where deterioration or damage of the bridge under consideration gradually increases). The anomaly data to be used as inputs in these cases, which are referred to as Case 1, Case 2, Case 3 and Case 4, respectively, are shown in

As an example,

Case 1 | A value equal to the average times 3 is given consecutively |
---|---|

Case 2 | A value equal to the average times 4 is given consecutively |

Case 3 | A value equal to the average times 5 is given consecutively |

Case 4 | Values equal to the average times 2, 3, 4, ∙∙∙, n are given consecutively |

Bridge” with the changes of the curve obtained by each moving average method in the case where Case 1 anomaly data are input consecutively. In order to quantify the detection sensitivity in the cases where Case 1 to Case 4 data on anomalies attributable to serious deterioration (damage) of the bridge of interest are input consecutively, the number of inputs needed by the curve obtained by each moving average method to reach the serious deterioration (damage) criterion level when Case 1 to Case 4 anomaly data are input consecutively was determined.

From

Case Number of inputs | Case 1 | Case 2 | Case 3 | Case 4 | |
---|---|---|---|---|---|

Number of inputs needed to reach Deterioration Phase 1 | Simple moving average method | 9 | 6 | 5 | 6 |

Weighted moving average method | 6 | 4 | 3 | 4 | |

Exponential moving average method | 6 | 4 | 3 | 4 | |

Number of inputs needed to reach Deterioration Phase 2 | Simple moving average method | 11 | 8 | 8 | |

Weighted moving average method | 8 | 5 | 6 | ||

Exponential moving average method | 10 | 6 | 6 |

however, indicate that in the long-term observation of “characteristic deflection”, the detection of serious deterioration (damage) of the bridge of interest might lag behind (i.e. lower sensitivity). In the next section, therefore, attention is turned to the slope of the “characteristic deflection” curve at a point of abrupt change after the second input of anomaly data as another possible indicator for damage detection, and detection sensitivity attainable by using that indicator is compared and evaluated.

In order to solve the above mentioned problem, it needs to find out an indicator capable of capturing a change in the characteristic deflection curve immediately after anomaly data are input to the system. Attention is turned, therefore, to the slope of the curve. To be more specific, a comparison is made focusing on the slope of the moving average curve after predetermined anomaly data are input two times consecutively. The reason why attention is turned to the curve after two consecutive inputs of anomaly data is as follows. As mentioned earlier, “characteristic deflection” varies considerably because of external disturbance factors. Consequently, it is not uncommon that calculated values of “characteristic deflection” change sharply from the previous input data. The aim, therefore, is to establish criteria by which to detect and evaluate (possible) serious deterioration (damage) if an abrupt change in characteristic deflection has been indicated by calculation two times consecutively. The anomaly data used here is the Deterioration Phase 1 value mentioned as an example in Section 3.2.3. To consider a wide range of conditions, the timing of the consecutive input of anomaly data was determined as follows. Of the characteristic deflection values calculated by the moving average method at or after the 15th input counted from the first input data from which “characteristic deflection” begins to change, a total of 10 points consisting of the five largest values and the five smallest values are selected. Then, the slopes of the curves obtained by inputting the anomaly data two times consecutively at each point are compared. Examples of the 10 points thus selected are shown with red circles (① to ⑩) in

As indicated by the results shown in

Statistical quantity Data | Average (mm) | Standard deviation (mm) | Variance | |
---|---|---|---|---|

All data | −5.218 | 1.733 | 3.004 | |

15 data sections | Simple moving average method | −5.108 | 0.212 | 0.045 |

Weighted moving average method | −5.117 | 0.310 | 0.096 | |

Exponential moving average method | −5.174 | 0.306 | 0.094 |

Moving average method Timing of anomaly data input* | Simple moving average method | Weighted moving average method | Exponential moving average method |
---|---|---|---|

① | −0.4770 | −0.6850 | −0.5924 |

② | −0.4486 | −0.7041 | −0.6820 |

③ | −0.4278 | −0.7117 | −0.6650 |

④ | −0.2693 | −0.7074 | −0.6705 |

⑤ | −0.4223 | −0.7012 | −0.6514 |

⑥ | −0.3840 | −0.7226 | −0.6923 |

⑦ | −0.3810 | −0.6980 | −0.6461 |

⑧ | −0.5293 | −0.7486 | −0.7359 |

⑨ | −0.3160 | −0.7725 | −0.7365 |

⑩ | −0.3593 | −0.7423 | -0.7172 |

*See

Difference Moving average method | Minimum difference | Maximum difference |
---|---|---|

Simple moving average method | 0.0060 | −0.2540 |

Weighted moving average method | −0.4305 | −0.4941 |

Exponential moving average method | −0.3412 | −0.4853 |

the weighted moving average method or the exponential moving average method is used, the slope of the curve tends to change noticeably when the anomaly data are input two times consecutively (see

As pointed out in Section 4.2.1, the method of damage detection relying solely on the number of inputs needed to reach the serious deterioration (damage) criterion level can result in a delay in taking response action. Using that method in conjunction with the method of using a newly employed damage indicator, namely, the slope of the “characteristic deflection” curve, will help improve the sensitivity in detecting serious bridge damage. In order to put that approach to practical use, it is necessary to establish rational serious deterioration (damage) criteria associated with the slope of the characteristic deflection curve.

To evaluate the sensitivity of “characteristic deflection” used by the bus monitoring system as a serious damage indicator, the influence of artificial damage (bridge guardrail removal) on “characteristic deflection” was evaluated by using a decommissioned bridge (a real old bridge to be removed shortly). This section deals with the field test results thus obtained. A simulation analysis using an analysis model allowing for the coupling between the bus and the bridge is also performed to calculate changes in the “characteristic deflection” of the artificially damaged bridge, and damage detection sensitivity is compared and evaluated from the analytical point of view.

The field test was carried out by using a 72-year-old (as of the time of the study) reinforced concrete bridge to be dismantled and removed shortly (“Sakae Bridge” [

out-bound lanes (15 in-bound runs and 15 out-bound runs) are calculated. Taking advantage of the absence of other road traffic (ideal condition) because the bridge was already in the process of demotion and removal, the measurements were conducted under various conditions by varying such conditions as vehicle weight and vehicle speed (constant speed).

The vehicles used for the measurement purpose are Ube-city’s sightseeing bus having a gross weight of about 15 tf and a mini vehicle having a gross weight of about 1.3 tf. As in the long-term field test mentioned earlier, an acceleration sensor was installed at a similar position (under the rear wheel spring) of each vehicle for continuous measurement. Besides this sensor, three other acceleration sensors of comparable performance were also installed [on the right side, at the center (normal location) and on the left side under the rear wheel spring] to evaluate the influence of sensor locations.

The “Sakae Bridge” used for the field measurement is a 168.3-meter-long, 11.0- meter-wide eight-span simple cantilever reinforced-concrete T-girder bridge completed in 1941 (managed by the Ministry of Land, Infrastructure, Transport and Tourism). For reconstruction, the bridge was demolished and removed by stages over a period of two years from fiscal year 2012.

This section puts together and discusses the results obtained from the field test conducted by using the decommissioned bridge as mentioned above.

Changes in characteristic deflection depending on the location of the acceleration sensor installed under the rear wheel spring were examined.

Items | Bus | Mini vehicle |
---|---|---|

Riding capacity | 57 persons | 2(4) |

Length | 1,194 cm | 339 cm |

Width | 249 cm | 147 cm |

Height | 330 cm | 189 cm |

Vehicle weight | 11,810 kg | 860 kg |

Gross weight | 14,945 kg | 1,320 kg |

Front axle weight | 4,030 kg | 440 kg |

Rear axle weight | 7,780 kg | 420 kg |

Items | Model No. | Serial No. | Channel | Axis | Sensitivity | Unit | Location |
---|---|---|---|---|---|---|---|

Bus | SA11ZSC-TI | 5692 | CH1 | Z | 0.99 | mV/ms^{−}² | Center |

M353B16 | 106161 | CH2 | Z | 1.058 | mV/ms^{−}² | Right | |

M353B12 | 106410 | CH3 | Z | 0.483 | mV/ms^{−}² | Left | |

Mini vehicle | 2422 | 1236 | CH1 | Z | 794.0 | mV/G | Center |

1237 | CH2 | Z | 795.1 | mV/G | Right | ||

1238 | CH3 | Z | 797.3 | mV/G | Left |

Length | L = 168.29 m |
---|---|

Width | W = 11.0 m (two lanes + sidewalks) |

W = 2.5 m (sidewalks) | |

Span | Eight spans |

Super structure | Cantilever reinforced concrete (RC) T-girder bridge |

locations under the rear wheel spring of the sightseeing bus. As

To evaluate the effects of vehicle axle weight on “characteristic deflection”, “characteristic deflection” was calculated from the acceleration responses of the sightseeing bus and the mini vehicle, which have a gross weight difference of more than 10 tf, measured while they were moving. As examples,

requirements of the bus monitoring system are not met (i.e. not suitable for the calculation of “characteristic deflection”).

In this section, 15 calculated values of “characteristic deflection” derived from the measurement results obtained at four different times of year by using the sightseeing bus having a gross weight of about 15 tf are used to examine the effects of the artificial damage (guardrail removal) introduced into the bridge (“Sakae Bridge”). As an example,

First, turning attention to Span 3 (see

Measurement No. Bridge crossing No. | Before guardrail removal | After guardrail removal | ||||||
---|---|---|---|---|---|---|---|---|

First Measurement Sept. 11, 2012 | Second Measurement Jan. 10, 2013 | Third Measurement Feb. 12, 2013 | Fourth Measurement March 5, 2013 | |||||

Span 2 | Span 3 | Span 2 | Span 3 | Span 2 | Span 3 | Span 2 | Span 3 | |

1 | −3.347 | −2.668 | −2.727 | −2.477 | −2.130 | −2.264 | −1.510 | −2.418 |

2 | −2.723 | −2.083 | −1.854 | −3.262 | −2.081 | −2.468 | −2.194 | −2.951 |

3 | −1.702 | −3.326 | −2.999 | −2.003 | −2.158 | −1.886 | −3.042 | −3.719 |

4 | −2.341 | −2.237 | −3.068 | −2.699 | −2.652 | −4.045 | −1.867 | −3.100 |

5 | −1.976 | −1.104 | −2.524 | −2.866 | −3.289 | −3.241 | −1.922 | −2.482 |

6 | −1.704 | −1.780 | −4.348 | −2.412 | −1.675 | −2.650 | −1.660 | −2.669 |

7 | −2.885 | −3.595 | −0.854 | −2.739 | −3.421 | −2.923 | −2.060 | −2.544 |

8 | −2.636 | −2.449 | −2.179 | −2.781 | −3.197 | −3.454 | −2.591 | −1.790 |

9 | −1.359 | −2.607 | −1.510 | −2.019 | −2.769 | −4.074 | −2.562 | −1.941 |

10 | −1.794 | −1.507 | −2.119 | −2.323 | −2.725 | −2.936 | −2.466 | −2.057 |

11 | −1.618 | −3.852 | −1.235 | −2.014 | −1.961 | −2.372 | −2.315 | −3.023 |

12 | −2.647 | −3.419 | −3.159 | −3.124 | −2.554 | −3.317 | −1.639 | −.872 |

13 | −2.819 | −3.075 | −2.720 | −2.710 | −2.522 | −2.477 | −1.604 | −3.404 |

14 | −1.568 | −3.043 | −1.409 | −3.183 | −2.334 | −3.094 | −2.398 | −3.583 |

15 | −3.421 | −3.321 | −1.636 | −2.593 | −3.606 | −3.337 | −3.815 | −4.857 |

Average | −2.303 | −2.671 | −2.289 | −2.614 | −2.605 | −2.969 | −2.243 | −2.894 |

bridge resulted in an increase in “characteristic deflection”. In Span 2, which includes a cantilever structure, however, the characteristic deflection value (−2.24) obtained after the fourth measurement is slightly smaller than the value obtained before the guardrail removal. Since Span 2 includes a cantilever structure, it is thought likely that the cantilever structure somehow influenced the “characteristic deflection”. When calculating characteristic deflection of a bridge with a cantilever structure, therefore, it is necessary to perform a simulation analysis and compare the calculated values with the analytical results.

This section deals with a model-based simulation analysis of the bridge measurement results obtained from the bus monitoring system performed to analytically identify the bridge behavior before and after the guardrail removal.

In the analysis, a static FEM analysis [

Case 1: With guardrails, no damage,

Case 2: Without guardrails, no damage,

Case 3: With guardrails, damaged,

Case 4: Without guardrails, damaged,

The analysis used MIDAS-GEN (MIDAS-IT Co.).

enlarged view of an assumed damage region in the “damaged” case shown in

Items | Conditions |
---|---|

Analysis method | Static elasticity analysis using 3D solid elements |

Material constants | JIS (RC) Fc24 or equivalent |

Modulus of elasticity: 2.2668E7 | |

Poisson’s ratio: 0.2 | |

Unit weight: 24.0 | |

Load | Midspan vertical downward unit concentrated load (1 kN) |

Boundary conditions | Simple beam; restrained at bottom of girder end |

Case | Deflection (≠ characteristic deflection) | Ratio to “with guardrail” case |
---|---|---|

1 | −2.89 × 10^{−6} m | |

2 | −3.00 × 10^{−6} m | 1.04 (4% increase) |

3 | −3.29 × 10^{−6} m | |

4 | −3.47 × 10^{−6} m | 1.05 (5% increase) |

Items | With guardrails | Without guardrails | Decrease ratio |
---|---|---|---|

Cross-sectional area (m^{2}) | 6650 | 5948 | 10.5% |

Neutral axis location (m) | 0.974 | 0.877 | |

Geometrical moment of inertia (m^{4}) | 1.612 | 0.980 | 39.2% |

The results of the 3D FEM analysis mentioned earlier show that the change ratio ranges from 1.04 to 1.05, and the effects of the guardrail removal are very small compared with the values obtained from the geometrical moment of inertia. This is thought to indicate that stress transmission paths extend two-dimen- sionally under the midspan loading due to the moving bus. This does not agree with the result of stiffness evaluation based on the assumption, for cross-sectional calculation, of Navier’s hypothesis (of plane sections remaining plane) and bending in the bridge axis direction.

Comparison with characteristic deflection reveals that the calculated values of “characteristic deflection” for Span 2 show decreases after the guardrail removal.

According to the “characteristic deflection” calculation results for Span 3, the “characteristic deflection” values obtained after the guardrail removal (average: 2.93 mm) indicate a decrease of about 10% compared with the characteristic deflection values (average: 2.64 mm) obtained before the guardrail removal. In view of the fact that the change ratio obtained through the static analysis in the case assuming general delamination of the underside of the girder was about 5%, it can be said that the “characteristic deflection” is reasonably sensitive to the guardrail removal. For Span 2, which includes a cantilever structure, however, the “characteristic deflection” after the guardrail removal showed a slight increase from the value obtained before the guardrail removal. This result is not consistent with the results of 3D FEM analysis, either. One likely reason for this is that the model used for the analysis consists of a simple span without a cantilever structure, while the calculated values of “characteristic deflection” have been influenced by the cantilever structure. Changes in measurement results due to seasonal factors are possible, but that seems unlikely in view of the results for Span 3. Span 2 requires further study, and it is also necessary to examine other factors such as the possible influence of the type of bridge structure on “characteristic deflection”.

These results have shown, at least at this stage of study in which the usefulness and validity of “characteristic deflection” as an evaluation indicator for use by the bus monitoring system is being verified, that the method of using a three- dimensional model for comparison and evaluation is useful when measuring a bridge in which stress transmission paths extend two-dimensionally.

Short and medium span bridges in Ube-city have been monitored over a long period of time by using the bus monitoring system operated in the city’s bus network. This paper has presented the results of long-term observation of “characteristic deflection” used as an evaluation indicator and described a newly developed characteristic deflection observation method. Damage detection performance of the “characteristic deflection” has also been verified systematically by introducing artificial damage (guardrail removal) into a decommissioned bridge that was in the process of demolition. Main findings of this study are summarized below:

1) For the purpose of long-term monitoring, a field test was conducted over a period of four years by using an in-service fixed-route bus. The observation of changes in “characteristic deflection” has not revealed any significant changes, and measurement needs to be continued for further observation.

2) On the basis of previous study results, the moving average method has been used to reduce fluctuations of “characteristic deflection” measurements in the bus monitoring system. The moving average method used in this study is the simple moving average method, which calculates simple averages for different data sections without weighting data. The observation of changes in “characteristic deflection” by the simple moving average method has not revealed any significant sharp changes. It can be concluded, at least at this stage, that none of the observed bridges has been seriously damaged.

3) The method of observing changes in “characteristic deflection” by the simple moving average method has been shown to have some problems. One of them is excessive smoothing out of data, and another is that the indication of serious damage by calculation results lags considerably behind real-time data. In order to solve these problems, the simple moving average method was evaluated through comparison with other moving average methods (weighted moving average method, exponential moving average method). Anomalous values were given to the moving average curves of “characteristic deflection” obtained by each moving average method, and the number of inputs needed to reach the deterioration criterion level was compared. As a result, it has been shown that the deterioration criterion level is reached earlier when weighted moving averages or exponential moving averages are used than when simple moving averages are used.

4) The comparison of the number of inputs needed to reach the deterioration criterion level raised concern about a possible delay in detecting an abrupt change in the condition of the bridge being monitored because a certain number of inputs were needed, regardless of the type of moving averages used, until the deterioration criterion level was reached. Attention was turned, therefore, to the slope of the moving average curve in making comparisons. As a result, by comparing the case where an abrupt change (assumed damage) is given and the case based on the actual changes observed thus far, it was concluded that differences indicated by simple moving averages are too small to detect an abrupt change in the bridge condition. In contrast, weighted moving averages and exponential moving averages showed certain degrees of difference. It has therefore been decided to use weighted moving averages, instead of simple moving averages, for “characteristic deflection” monitoring because weighted moving averages tend to show greater differences.

5) On the basis of the results mentioned above, as a next step it is necessary, judging from the slope data (slope of the weighted moving average curve: approx. −0.7) obtained in this study, to develop a new set of deterioration evaluation criteria, without sticking to the current deterioration evaluation criteria.

6) In the field test conducted by using a bridge that was decommissioned and was undergoing demolition, the influence (sensitivity) of artificial damage (guardrail removal) on “characteristic deflection” was evaluated. The results obtained for Span 3 of the bridge confirmed that “characteristic deflection” is reasonably sensitive to a decrease in the flexural stiffness of the entire bridge. In the case of Span 2, which includes a cantilever structure, however, the “characteristic deflection” before the guardrail removal was greater than the “characteristic deflection” after the guardrail removal. This result is not consistent with the 3D FEM analysis results, either. It is likely that the calculated values of “characteristic deflection” have been influenced by the cantilever structure. Further study is needed, therefore, particularly on the influence of the type of bridge structure on “characteristic deflection”.

7) At present, the usefulness and validity of “characteristic deflection” as an evaluation indicator are being evaluated and verified. It can be concluded, at least at this stage, that the method of using a three-dimensional model for comparison and evaluation is useful when measuring a bridge in which stress transmission paths extend two-dimensionally.

Finally, the long-term field test of the bus monitoring system has produced useful results associated with the practical application of the system although a number of problems still remain to be solved. Important challenges for the bus monitoring system include the automation and rationalization of measurement. The structural health evaluation of bridges can be made simpler and more efficient by analyzing and solving the problems identified as a result of this study.

The authors would like to express our sincere thanks to Dr. Emoto, H. of Kouzoubutsu Clinic Co., Ltd. for his great support on this research work, and also to the Traffic Division of Ube-city’s Transportation Bureau for the generous cooperation in connection with the long-term field test conducted for the purposes of this study including bus sensor installation and bus operation arrangements.

Miyamoto, A., Puttonen, J. and Yabe, A. (2017) Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity. Engineering, 9, 68-122. https://doi.org/10.4236/eng.2017.92005