POLITECNICO DI MILANO
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
DOCTORAL PROGRAMME IN ENVIRONMENTAL AND INFRASTRUCTURE ENGINEERING
DECISION
SUPPORT
SYSTEM
FOR
HYDRAULIC
RISK
MANAGEMENT
OF
A
RIVER
BRIDGE
Doctoral Dissertation of: GIANLUCA CROTTI
Supervisor:
Prof. FRANCESCO BALLIO Tutor:
Prof. STEFANO MALAVASI
The chair of the Doctoral Program: Prof. RICCARDO BARZAGHI
Dedicated to my Family
Alessandra
Martina, Aurora & Leila
A
BSTRACTA number of river bridges collapses worldwide every year during flood events, due to combination of actions including traffic loads, water and wind load, river bed degradation, accumulation of debris. Incidence of failure is higher for relatively older bridges which may have been designed without adequate consideration for some of such actions, in particular the scour potential at piers and abutments; in this case, consolidation of bridge foundations may be required. As an alternative to structural rehabilitation we propose here a Decision Support System (DSS): a non‐ structural real time risk mitigation dataflow that aids the bridge managers to decide whether a bridge should be partially or totally closed to traffic. Regardless of the structure to manage, the DSS is basically composed by five conceptual blocks: (i) a real time monitoring system focusing on the evaluation of the environmental actions on the structure rather than on the health state of the structure itself: such choice allows sufficient lead time for bridge closure (such action may not prevent the damage of the structure but should avoid casualties); (ii) a data analysis tool to evaluate the reliability and validation of the data; (iii) a structural model to determine how far the structure is from collapse; (iv) a forecasting scenario to indicate how much time is left to exit from the safe operational domain of the structure and procedures to manage the structure from the normalcy to the emergency state. Then (v) a field application is presented and discussed. The methodology is presented with reference to the field case of a bridge over the river Po (Italy); its generalization to a larger variety of conditions is also discussed.
IV
C
ONTENTS Abstract ...III Contents ... IV Introduction ... ‐ 1 ‐ I.1. Decision Support System (DSS) ... ‐ 1 ‐ I.2. Field case: Borgoforte bridge ... ‐ 4 ‐ I.3. Presentation of the Chapters ... ‐ 7 ‐ I.4. References ... ‐ 9 ‐ Chapter 1: Instrumentation ... ‐ 10 ‐ 1.1 Standard devices: anemometer, hydrometer and video camera ... ‐ 13 ‐ 1.2 Non standard device: echo sounder ... ‐ 15 ‐ 1.3 Innovative device: BLESS sedimeter ... ‐ 17 ‐ 1.4 References ... ‐ 20 ‐ Chapter 2: Data analysis ... ‐ 22 ‐ 2.1 Anemometer ... ‐ 22 ‐ 2.2 Hydrometer ... ‐ 25 ‐ 2.3 Echo sounder ... ‐ 28 ‐ 2.4 BLESS ... ‐ 38 ‐ 2.5 Video camera ... ‐ 51 ‐ 2.6 References ... ‐ 55 ‐ Chapter 3: Structural model ... ‐ 56 ‐ 3.1 Geometry ... ‐ 56 ‐ 3.2 Actions and environmental loads ... ‐ 58 ‐ 3.3 Structural model and collapse mechanism ... ‐ 61 ‐ 3.4 Safety coefficient ... ‐ 63 ‐ 3.5 References ... ‐ 66 ‐ Chapter 4: Bridge risk management ... ‐ 68 ‐ 4.1 Bridge management procedures: general approach ... ‐ 68 ‐ 4.2 Scenarios ... ‐ 71 ‐4.3 Procedure examples ... ‐ 73 ‐ 4.4 References ... ‐ 76 ‐ Chapter 5: Field applications ... ‐ 77 ‐ 5.1 Back analysis of year 2014 ... ‐ 77 ‐ 5.2 Synthetic event (warning reached) ... ‐ 80 ‐ Chapter 6: Discussion ... ‐ 83 ‐ 6.1 The role of time in safety management of constructions: a conceptual framework ... ‐ 83 ‐ 6.2 Generalization of the approach to other systems ... ‐ 85 ‐ 6.3 Costs and benefits ... ‐ 86 ‐ 6.4 References ... ‐ 87 ‐ Conclusions ... ‐ 88 ‐
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I
NTRODUCTION
The present introduction is organized in three conceptual blocks: at the beginning we present and discuss, from the general point of view, the conceptual problem analyzed in this doctoral work and the solution proposed, that is the Decision Support System (DSS). Then we focus the attention on the specific field case in which we have developed the DSS dataflow. At the end a short description of the Chapters is illustrated.I.1.
Decision Support System (DSS)
Statistics of bridge collapse in several countries around the world indicate hydraulic processes as the main triggering cause. Wardhana and Hadipriono[1] state that "Over 500 failures of bridgestructures in the United States between 1989 and 2000 the most frequent causes (…) were attributed to floods and collisions. Flood and scour (…) contributed for almost 53% of all failures. Bridge overload and lateral impact forces from trucks, barges/ships, and trains constitute 20% of the total bridge failures. Other frequent principal causes are design, detailing, construction, material, and maintenance." This study confirms results of the previous analysis in Rhodes and Trent[2], who indicated an average annual flood damage repair costs of approximately $50 million
for highways on the Federal‐aid system”. In addition to scour authors underline the role of morphological evolution of the rivers, so that bridges "experience problems with aggradation, degradation, bank erosion, and lateral channel shift during their useful life." The relevance of scour on bridge failure in the USA has been recently analysed in detail by Briaud et al.[3]. A worldwide
dataset[4] shows that natural hazards are the main causes of bridge collapse, with flooding or scour
being responsible for around 60% of the events. Scour has also been indicated as the first responsible for bridge collapse in New Zealand[5,6]. Similar results for the Italian road and railway
systems are reported in Ballio et al.[7]. Azhari et Loh[8] and Bao et Liu[9] show cases from other
countries in the world, confirming that bed level variations and local scour around bridge foundations are predominant factors in bridge failures.
Historical evidence of vulnerability of river bridges has raised attention of the technical community on hydraulic processes: a comparative analysis of some national building and maintenance codes over the last 20 years reveals that, with respect to the past, recent codes provide more explicit and punctual indications to designers and managers on how to evaluate and take into consideration the interactions between the river system and the structure[10]. As stated in Arneson et al.[11] "The added
cost of making a bridge less vulnerable to scour is small when compared to the total cost of a failure which can easily be two to ten times the cost of the bridge itself. Moreover, the need to ensure
public safety and minimize the adverse effects resulting from bridge closures requires our best efforts to improve the state‐of‐practice for designing and maintaining bridge foundations to resist the effects of scour." Evolution of codes is expected to grant higher level of safety for bridges (some evidence comes from a time series analysis in Briaud et al.[3]). However, it also makes several existing structures to be no more compliant with the new standards; considering the increase of the nominal traffic and wind
loads in the last sixty years and the modification of reference hydraulic hazard scenarios due to climate change[12]. Therefore, "because it is not economically feasible to construct all bridges to
resist all conceivable floods, or to install scour countermeasures at all existing bridges to ensure absolute invulnerability from scour damage, some risks of failure from future floods may have to be accepted"[11].
How can we manage a bridge that is no more compliant with the new standards? While one might
regretfully accept the possibility of a bridge failure, failure‐induced casualties must be instead avoided. To handle this situation there are, basically, three ways: i) to rebuild the bridge ii) to reinforce the bridge to increase its resistance and so respecting the new standards or iii) to use a monitoring system and associated procedures to manage the structure anytime. Depending on the specific case it is also possible to combine different strategies. The last one considers real time management of river bridges as a non‐structural risk mitigation measure, alternative (or in addition) to the standard structural countermeasures. The implementation of such a Decision Support
System (DSS) is the key scope of this thesis. More specifically, we propose a DSS, based on a
monitoring infrastructure, which helps the bridge managers decide whether a bridge should be partially or totally closed to traffic due to a forecast of particularly harsh environmental conditions which may lead the structure to operate out of its safe operational zone. Bridge management may not prevent the damage of the structure but should avoid casualties. Such a strategy is consistent with the indications of the Eurocode EN 1990[13] which states (clause 2.2) that “the measures to
prevent potential causes of failure and/or reduce their consequences may, in appropriate circumstances, be interchanged to a limited extent provided that the required reliability levels are maintained.” In fact, the possibility of limiting risk by some active management of the system connected to expected scenarios of environmental conditions rather than by intrinsic, passive, resistance to actions is an accepted practise for a variety of systems. Significant examples are preventive evacuations for hurricanes, mobile levees systems for river floods, the “MOSE” system against high tides in Venice, speed reduction for high‐speed trains rather than stopping wind‐mills or harbour cranes under extreme wind conditions. The specific case study under discussion is a road bridge across the Po river, Italy. The structure, built in the sixties, is relatively well preserved; however, traffic and wind nominal loads are now larger than the design values and there is historical evidence of strong variation of the river bed elevation at the bridge cross‐section, which was not originally accounted for. Analysis of past events and the simulation of possible future scenarios show that a proper management protocol of closure
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with respect to casualties without significant impact of the functionality onto the infrastructure. Real time monitoring systems for the evaluation of vulnerability of river bridges have been already proposed in the literature[14,15]. A peculiar feature of present proposal is that the monitoring system
is focussed on the evaluation of the environmental actions on the structure rather than on the health state of the structure itself. Such a choice is in this case necessary, as warning based on variation of properties of the structure (a classic Structural Health Monitoring, SHM) would not give sufficient lead time to perform the actions that are necessary for bridge closure. With respect to a structural pier reinforcement, the non‐structural strategy here adopted is advantageous in that: (i) it is less expensive, (ii) through a continuous monitoring it provides a deeper knowledge of the environmental actions on the structure, (iii) it is more flexible and adaptable to future changes of the environmental scenarios. The necessity of a technological system and a management plan for guaranteeing the required safety constitutes the obvious drawback of the proposed solution. From this point of view, time becomes the most important parameter to control. Knowing the stress state of the structure anytime (now and in a forecasted evolution), thanks to the monitoring system we can develop procedures to manage the bridge from normalcy to emergencies (floods) possibly arriving at its closure. Basically, The DSS dataflow has to answer at least three questions: “is now the bridge in safety condition?”. We need a real time monitoring system together with a structural model to evaluate the stress state of the structure anytime, also computing a safety coefficient of the bridge; “how much time do we have before the potential bridge collapse?”. We need a scenario forecasting to understand the evolution, in time, of the safety coefficient; “how can we manage a bridge anytime?”. We need an emergency procedures according to the scenario forecasting to manage the structure for every safety coefficient time evolution leading to the complete closure of the bridge.
I.2. Field case: Borgoforte bridge
Borgoforte is a small village in the north of Italy (Mantova province, Lombardy), close to the biggest Italian river, the Po (Figures I.1 and I.2). The structure under consideration is a state road bridge crossing the river Po, joining Mantova to Modena (the right one in the Figure I.1). The total length of the bridge is 630 m, while for normal flow conditions (as shown in the Figure I.1) the river is about 300 m wide, so that only four of the 44 piers have permanently submerged foundations (Figure I.2), the remaining ones being positioned on the flood plains. Fig. I.1 Borgoforte bridge location Fig. I.2 Borgoforte road bridge on the left (photo from the downstream right bank) and, on the right, the map of the bridge with the underwater pier numberedIn November 2000 an important flood occurred. The discharge was around 12000 m3/s, roughly
corresponding to a 100‐year return period. Three months later, in February 2001, a bathymetry evidenced a 15‐meter scour hole close to pier 32. Moreover, a general degradation of the river bed level also involved piers 31 and 33. Consequently, a strength problem had to be addressed. The pier can be modelled as a cantilever
30
31
32
33
flow direction Road bridge Rail bridge‐ 5 ‐ the bridge administration completed the stabilization works (pier reinforcement) for three piers in water (31, 32 and 33), Figure I.3. Fig. I.3 Piers of the bridge. Left pier 30 (not stabilized). Right an example of stabilized pier (piers 31, 32 and 33) A “rip‐rap” for pier 32 only was positioned. This countermeasure is made by loose stones deposited under water around the pier to create a heavy and armoured foundation against scour. In Figure I.2 right, a bridge top view, it is possible to see that the three piers are now larger when compared to that labelled as number 30. A hydraulic study followed the event[16], declaring that the reinforced piers can now easily survive a flood similar to that of November 2000. The study also highlights how the possibility of scour problems under heavy floods can also involve pier 30, even if it is close to the river bank. The standard approach for the diagnosis of the safety conditions of an existing construction (in this case the pier number 30, Figure I.2) is basically equivalent to the design of a new construction. The key point is that the bridge was built in the 1961 using old codes but the diagnosis of the safety condition must be referred, obviously, to the actual codes (Eurocode EN 1990[13]). The main
differences are i) the old approach ASD (Allowable Stress Design) is replaced by ULS method (Ultimate Limit States) and ii) the old code did not consider a bed variation while the new one takes it into account. In a nutshell, the first point changes not only the load combinations but also the coefficients of the actions that compose these combinations. The result is that some of the new combinations are more severe compare to the older ones. The second point means that the new bridges are designed to withstand a bed variation, within certain limits. The result can be summed up in Figure I.4 where the residual moment coefficient δ (that represents the distance of each point from the resistance domain in terms of bending moment, see Chapter 3) is plotted against degradation of the riverbed with respect to its reference value (that is for convenience set to zero to highlight the variation). A coefficient equal to 0.5 means that the structure is stressed to 50% of its maximum. It is clear that the response of the structure to different codes is totally different. Even though we avoid the degradation of the river bed (degradation equal to zero) the residual coefficient changes from 0.5 (using old code) to 0.3 with Eurocode (due to different load combinations). Moreover, if we consider also a bed variation, the pier reaches the limit zone with only three meter
of scour (compared to 8 meter using the old code). Considering that in this hydraulic section a variation of two meters can be considered as a normal bed fluctuation (Chapter 2, Section 2.3), the bridge condition requires attention. Fig. I.4 Residual moment coefficient as a function of the river bed lowering with respect to the reference elevation Nevertheless, a specification is required: with three meter of scour the bridge arrives at its limit (δ = 0) only if the specific load combination (the worst one, presented by the grey line) is effectively acted on the bridge; on the contrary we are in the safe zone (δ > 0) if the real traffic is “lighter”. Unfortunately, without a monitoring system this information is unknown. The allocated budget (around 2 M€) for the stabilization works of piers 31, 32 and 33 (Figure I.3) was not enough to allow the reinforcement of this last pier so the bridge administration (Province of Mantova) decided to implement a Decision Support System that helps not only to manage this residual risk during floods, but also to understand if any stabilization plan is needed for this pier, due to possible new floods. ‐1.2 ‐1.0 ‐0.8 ‐0.6 ‐0.4 ‐0.2 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2 3 4 5 6 7 8 9 10 R e si dua l m o m e nt c oe ff ic ie nt δ Degradation of the river bed (m) ULS (Eurocode) ASD (1962)
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I.3. Presentation of the Chapters
The structure of the DSS applied to the Borgoforte case is presented in Figure I.5 and, basically, it represents the index of this work. In the following we introduce all the five blocks to understand their structures and discuss their main topics. Fig. I.5 Borgoforte DSS (Decision Support System) dataflow Instrumentation Data analysis Structural model Bridge risk managment Field applications1
2
3
4
5
The first block is described in Chapter 1, where the monitoring system and all the installed instruments are presented. As discussed before, the goal of the monitoring is to measure quantities to evaluate loads against the pier 30 and the portion of deck of its competence. At the beginning there is an introduction that describes the main parameters involved in the load evaluations and the correlated devices. Then, the layout of the monitoring system is illustrated with a description and an output example for every device.
The second block is described in Chapter 2, where the data treatment is presented for all the devices. Among them, there are two devices, the echo sounder and BLESS (see Chapter 1), that need a data validation for two different issues. The echo sounder cannot be considered a standard device if it is used to measure the bed level variation close to a submerged structure, like a pier. The literature shows (see Chapter 2) some drawbacks that can negatively affect the signal of the device. The BLESS is a prototype so it needs a data validation by default. Such a data validation and a long period analysis are presented to discuss the relevance of (i) understanding if a device is working well and (ii) showing some details that can be carried out from 4‐5 years of data. The third block is described in Chapter 3, where the structural model is presented and discussed. In this Chapter the key point is not the structural model itself, because it can change in time, but its importance to summarize the information from the monitoring system. If the final goal of this work is to create something (DSS) that must help the bridge administration, we have to create an output that is really useful to the end user. The fourth block is described in Chapter 4, where the principles of the bridge management approach are given. Three issues are presented: (i) a general approach to the bridge management, with the phases of the emergency procedures and the definition of the thresholds; (ii) the adopted scenarios to forecast the evolution of the stress state of the bridge in time and (iii) a qualitative example of the procedures to understand the working mechanisms. The fifth block is described in Chapter 5, where field applications are presented considering a back analysis on an earlier year and a synthetic scenario where the alert phase is reached.
The sixth Chapter (not present in Figure I.5) provides an overall discussion about bridge risk management. The purpose is to highlight the potentiality of this approach (created specifically for the Borgoforte Bridge) thus supporting its generalization. Final Conclusions are then provided.
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I.4. References
[1] K. Wardhana, F.C. Hadipriono, (2003) Analysis of Recent Bridge Failures in the United States. Journal of Performance of Constructed Facilities, 17, 144-150. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:3(144)
[2] J. Rhodes, R. Trent, (1993) Economics of Floods, Scour, and Bridge Failures Hydraulic Engineering, Proceeding of the ASCE National Conference 1993, 1, 928‐933.
[3] J. L. Briaud, P. Gardoni, C. Yao, (2013). Statistical, risk, and reliability analyses of bridge scour. Journal of Geotechnical and Geoenvironmental Engineering, 140(2), 04013011.
[4] D. Imhof, (2004) Risk assessment of existing bridge structures. PhD Thesis, University of Cambridge, UK.
[5] G. H. Macky, (1990) Survey of roading expenditure due to scour. Report CR 90.09, DSIR Hydrology Centre, Christchurch, New Zealand. [6] B. W. Melville, S.E. Coleman, (2000) Bridge scour. Water Resources Publications, LLC, Highlands Ranch, Colorado, USA. [7] F. Ballio, A. Bianchi, S. Franzetti, F. De Falco, M. Mancini, (1998) Vulnerabilità idraulica di ponti fluviali. XXVI Convegno Nazionale di Idraulica e Costruzioni Idrauliche, Catania, Italy, 1998, 3, 69‐80.
[8] F. Azhari, K.J. Loh, (2017) Laboratory validation of buried piezoelectric scour sensing rods. Structural Control and Health Monitoring. 2017, 24(9), e1969 1‐14. [9] T. Bao, Z. Liu, (2017) Vibration‐based bridge scour detection: A review. Structural Control and Health Monitoring. 2017, 24(7), e1937 1‐19. [10] L.W. Zevenbergen, L.A. Arneson, J.H. Hunt, A.C. Miller, (2012) Hydraulic design of safe bridges. FHWA‐HIF‐12‐018 ‐ HDS‐7, FHA Washington D.C. [11] L.A. Arneson, L.W. Zevenbergen, P.F. Lagasse, P.E. Clopper, (2012) Evaluating scour at bridges. Fifth Edition", FHWA‐HIF‐12‐003 ‐ HEC‐18, FHA Washington D.C. [12] K. Alexandre, L. A. Garrow, M. J. Higgins, M. D. Meyer, (2013) Impacts of climate change on scour‐vulnerable bridges: Assessment based on HYRISK. Journal of Infrastructure Systems, 19(2). [13] EN 1990, Basis of structural design 2002, Eurocode, CEN. [14] T.K Lin, Y.S. Chang, (2017) Development of a real‐time scour monitoring system for bridge safety evaluation. Mechanical Systems and Signal Processing, 82, 503‐518.
[15] S.H. Ju, (2013) Determination of scoured bridge natural frequencies with soil–structure interaction. Soil Dynamics and Earthquake Engineering, 55, 247‐254.
[16] Provincia di Mantova (2008), Studio della vulnerabilità idraulica ponte di Borgoforte sul fiume Po.
Chapter 1:
Instrumentation
Considering that all the devices are installed to quantify the loads against the pier 30 and the portion of the deck of its competence, it is necessary, using a qualitative relationship, to present all the forces applied to the structure and the main parameters involved.
Load from vehicles (no device): Fload = f(Geometry, Ttraffic). It is applied at the deck level of the bridge
and depends on the daily bridge traffic. For this evaluation it is possible to use traffic monitoring information or using the standard bridge codes. In the absence of a direct measurement of traffic, load combinations given by national standards were adopted. There are different scenarios depending upon the number of load combinations. If the bridge is open (Ttraffic = 1, while it is Ttraffic = 0 with the bridge
closed), we consider the vehicle weight and the correlated bending moments. The force and bending moments due to truck braking are also taken into account together with the pier and deck weight.
Wind drag force (anemometer): Fwind = f(Geometry,Vwind, Ttraffic). It applies on the entire structure of the
bridge over the water surface and on the vehicles (trucks) crossing the bridge. Its direction and value depend on the wind blow that can change in time. The wind force depends on two main parameters beyond the geometry of the structure (Chapter 3): the wind speed (Vwind), normal to the bridge deck’s
main axis, and the presence of traffic increasing the bridge cross section. The first is an output from the monitoring system (using a standard anemometer): it is easy to calculate the normal load once the wind direction and bridge orientation are known. For Ttraffic, a binary value is set to 1 if the bridge is totally or
partially open and the cross section exposed to the wind is increased according to given tables (load combinations).
Water drag force (hydrometer + video camera): Fwater = f(Geometry, hwater, Vwater, Ddebris). The force
exerted by water is only related to the piers because the deck is above the maximum water level. This force depends on two independent parameters: the flowing stream level (hwater) and the presence of
debris upstream of the pier (Ddebris is a binary value 0/1) while the velocity of the flow is directly
correlated to the first one. The water level is given by a standard hydrometer. Using a rating curve and a 2D simulation for the river close to the bridge, one obtains the relationship between depth and water velocity. Debris accumulation is not so uncommon and its effect depends on both the river basin characteristics (the possibility that trees and/or small bushes “fall” into the river) and the pier geometry (that can facilitate the accumulation). The presence of debris changes the water load contribution because for the water point of view the area of the pier depends on the quantity of debris. A standard video camera helps us detect their presence, in which case Ddebris is set to 1. Moreover, to evaluate the
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portion of the pier exposed to the flowing water it is necessary to know the riverbed level close to the pier, since hwater = Levelwater ‐ Levelriverbed.
Riverbed level (echo sounder and BLESS): its position influences the part of the pier exposed to the
water load and thus changes the length of the pier (so the dynamic bridge response to the external loads). This information is very difficult to obtain because, nowadays, no device can be considered standard for this measurement[17,18]. The literature is rich of papers about new prototypes tested in
laboratory and reports where instruments were tested in the field[11]. All of these devices have
advantages and disadvantages[19] but, in reality, no one can guarantee a reliable riverbed measurement
during floods. In the Borgoforte monitoring system, we install an echo sounder because it can be considered the best choice for this measure even though it could have working problems during floods (see Chapter 2). In addition, we install a device, called BLESS (Bed LEvel Seeking System), patented by Politecnico di Milano (Chapter 2). It helps to detect the bed level through different technology and working principle with respect to the echo sounder. All the instruments presented in the following Sections are parts of the monitoring system installed in 2009 on the Borgoforte Bridge. Figure 1.1 illustrates the monitoring system layout. Fig. 1.1 Monitoring system layout The monitoring system of pier 30 involves many sensors (Fig. 1.1). The wind anemometer provides both intensity and direction of the wind (Section 1.1). The hydrometer, between pier 30 and 31, furnishes the water level of the river Po (Section 1.1). Two video cameras are placed upstream of the bridge to monitor the accumulation of debris (Section 1.1). The echo sounder (Section 1.2) and the BLESS sedimeter (Section 1.3) measure the riverbed level downstream of pier 30.
30
Flow
29
31
32
33
34
Control room
Remote
station
Hydrometer Echo‐sounder Video cameras
The whole system is managed from a control room (Fig. 1.2) This is located under the bridge, between two main beams well above the maximum water level (Fig. 1.2). A real time device serves to collect data from all the mentioned devices, also managing storage, alarm generation and connections with the outside world. A wireless connection is used to send data to any remote station. Figure 1.2, on the right, shows the data flux of the monitoring system. The anemometer, the hydrometer and the echo sounder provide data every second while the timing of the BLESS and video camera can be set by the end user. The control room collects all the device signals and creates a summary file. Also in this case the end user can change the sampling rate of this file and the transmission frequency. Fig. 1.2 Control room at pier 34 on the left. Data flux on the right
The instrumentation (Fig. 1.1) can be classified into three main groups. The first includes standard devices like the anemometer the hydrometer and the video cameras (Section 1.1). In this context a device is called “standard” if it has these two characteristics. First, the device is used to measure exactly what it was built for: for instance, the anemometer is designed to measure wind velocity and its direction. Second, the device installation follows its user guide.
The second instrument group, called “non‐standard”, consists of a single device, the echo sounder (Section 1.2). This is a common device to determine river bed level but it is not really suitable to detect the river bottom position near structures such as piles and abutments. The presence of air bubbles, suspended sediments, turbulence, etc., especially during flood, can create false signals[20].
The last group includes an innovative device called BLESS, Bed LEvel Seeking System. The BLESS sedimeter[21], patented by the Politecnico di Milano[22], detects the river bed level downstream of the pier (more details are given in Section 1.3). As already discussed, the use of the echo sounder to measure scour (or the river bed level) is not really a standard because it does not guarantee satisfactory measurement accuracy during floods. BLESS helps to improve reliability by providing more robust data, especially during extreme events. Device and sampling rate Anemometer 1s Echo sounder 1s BLESS Video camera Data managing and packaging User setting In‐situ software Remote station File transfer 1 hour Monitoring system: Data flux Hydrometer 1s
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1.1 Standard devices: anemometer, hydrometer and video camera
To measure the wind characteristics, we use a common anemometer. A cup anemometer is mounted over the bridge deck (Figure 1.3 on the left), measuring the wind intensity (range: 0.4‐60 m/s; resolution: 0.1 m/s) and direction (range: 0‐360°; resolution: 0.4°) at a frequency of 1 Hz. The wind average and the gust speed (the peak value over the time record of interest) are given on a 10 min base, as recommended by specific standards to study wind actions on structures[23]. Figure 1.3, on the right, shows an example of a day plot (November 15th, 2013). Every 10 minutes we have the average of the wind speed (red) and the corresponding maximum value, called wind gust (green). In black is plotted the mean value of the wind direction (10 minutes based), intended as the direction from which the wind arrives. Fig. 1.3 The anemometer on the left and an example of a day plot on the right The device adopted for the water level measurement is a radar water elevation gauge (maximum range: 35 m; resolution: 1 mm; frequency: 1 Hz.). It is mounted at the side of the bridge deck, measuring water elevation approximately at the center of the river cross‐section (Figure 1.4). The water level average is given on a 1‐hour base but the timing can be modified by the end user of the monitoring.
Fig. 1.4 The hydrometer on the left and an example of a water level during November 2014 To evaluate the water drag force, we also need the water velocity. Even though this parameter is not correlated to a specific device we report some details here for the sake of completeness. In the Borgoforte cross section we have a level‐velocity relationship thanks to two previous works about the Borgoforte bridge. The first one[24] provides a rating curve as a relationship between the water level and the discharge, Q = a*h2+b*h+c, where Q is the discharge (m3/s), h is the water level (m asl) and a, b and c are parameters whose values are summarized in Table 1.1. The equation is divided into three parts to achieve a best fit of the acquired data. Table 1.1 Coefficient of interpolated rating curve Equation Coefficients Part 1 (below 17 m asl) Part 2 (from 17 to 22 m asl) Part 3 (above 22 m als) a 46 60 96 b ‐902 ‐1350 ‐2800 c 4658 8000 22500 In the second reference[16], there are results of a 2D simulation (using River2D software) to create the velocity maps close to the bridge pier number 30. Table 1.2 shows four velocity values and the corresponding discharges. Table 1.2 Discharge and corresponding velocity values Discharge (m3/s) Velocity close to pier 30 (m/s) Discharge (m3/s) Velocity close to pier 30 (m/s) 250 0.2 5400 2.8 510 0.3 10000 3.6 Using a linear interpolation between two consecutive points it is possible to obtain the velocity of the water V (m/s) for every discharge value Q (m3/s). Finally, from the water level we have the correlated discharge value, thanks to the rating curve, and from discharge we calculate the velocity, thanks to the 2D simulation. 12 16 20 24 0 3 6 9 12 15 18 21 24 27 30 m a sl days November 2014 BORGOFORTE Hydrometer
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Debris piling is one of the main parameters to be considered for bridge safety, as this heap can influence the hydrodynamic load and local scour[25]. This is the reason why two video cameras (3 Mpixel; lens: FJYV4.3×2.8SA‐SA2) taking images upstream of pier 30 were installed (Figure 1.5 is an example of a photo shot by the camera). Debris accumulation is dangerous[26] because it can increase the pier cross section with two negative effects. First, an increment in the resisting area also increases the hydrodynamic load over the bridge. Second, local scour around a wide pier is higher than around a narrow one[6,27]. Fig. 1.5 Example of drift upstream of pier 30 The goal of the video camera images is to detect automatically the presence of debris upstream of the pier and, consequently, an increase of the pier cross section to the water drag force (Chapter 3). The impact of the debris to scour around the pier is taken into account using a specific device (next Section) that measures, directly, the riverbed level.
1.2 Non standard device: echo sounder
The river bed level close to piers is the most relevant parameter for bridge safety and, at the same time, the most difficult to be obtained with high reliability. The echo sounder used in Borgoforte (beam: 14° conical; range: 0.8‐100 m; resolution: 0.01 m; frequency: 1Hz) produces an output filtered by an internal software that is inaccessible to the user, who thus cannot try to improve the output through proper regulations. More complex and expensive echo sounders rely on software having many parameters that are hardly associated to a real physical output. The adopted echo sounder is mounted on the rear face of pier 30 and measures the river bed elevation. Maximum scour depths are expected in front of the pier, but in such position the sensor would be exposed to floating debris which could damage it or mask its signal (Fig. 1.5). The echo sounder’s positioning and orientation have also proven to be critical issues [24]. The signal beam opens in a conical shape along its path in the water. Multiple reflections can begenerated from the bridge pier, and therefore misrepresenting the real bottom position is a real risk. For this reason, a major problem was to design the structure to fix the sensor to the bridge pier. This support must ensure the chance to remove the device for maintenance. This is an important task, as the sensor is immersed in flowing water most of the time. At the same time the structure has to provide the required mounting robustness. Photo (a) in Figure 1.6 shows the stainless steel tube that links the control box, at the deck level, to the echo sounder. Photo (b) is a zoom at the end of the tube where the echo sounder is installed, under the water surface. The device is located downstream of the pier, aligned to the pillars, to ensure higher protection against floating debris. As discussed, the device must be properly tilted to avoid any pier spurious reflection (Figure 1.6, c). The level of the device must be a compromise between operational and maintenance needs, as the echo sounder should stay underwater most of the year but its level has to allow inspection during dry low water periods. A special joint was designed at the lower end of the tube. Hinges allow the echo sounder’s removal and rotation, using a tie‐rod from the bank (Figure 1.6, d). Fig. 1.6 Plot of echo sounder and hydrometer data. (a) Echo sounder stainless steel tube. (b) Zoom at the end of the tube. (c) Detail of orientation. (d) Tube hinge
The plot in Figure 1.6 shows an example of the water (blue points) and bed levels (brown points) recorded during a flood. This plot is a good representation of the scour phenomena. Velocity increases when the water level rises. Vortexes around the pier exert a force high enough to dig sediments away from the structure[6], so the river bel level around the pier decreases.
(a)
‐ 17 ‐
1.3 Innovative device: BLESS sedimeter
In Borgoforte, we had the opportunity to install an innovative sedimeter. It was designed to measure the river bed level in close proximity to a submerged structure, such as a pier. The patented device called BLESS, Bed LEvel Seeking System, is based on Fiber Bragg Gratings (FBG)[28]. In a nutshell, the FBG can be described as a wavelength reflector. A light beam is emitted by a source into the fiber optic, and the grating reflects a particular wavelength that depends on the geometrical features of the Bragg gratings. A change in a grating properties (compression or extension) involves a change of the Bragg wavelength. In case of more than one sensor in the same fiber, like BLESS, every FBG has different initial wavelength and each wavelength range does not overlap with another ones). So the FBG can be used both for strain and temperature measurements. Creating BLESS requires three main steps: BLESS measures temperature values, so the fiber is placed inside a stainless steel tube to avoid any strain effect (Figure 1.7 A); BLESS measures also different heat dissipations, so we add electrical circuits outside the steel tube (Figure 1.7 B); BLESS is both buried in the bed and immerged in the flowing water, so it needs an electrical insulation (Figure 1.7 C). Fig. 1.7 BLESS construction: (A) fiber inside the stainless steel tube; (B) adding electrical circuits and (C) electrical insulation The Figure 1.7 shows the main steps to build the BLESS sedimeter. Pictures are referred to a BLESS prorotype used in the lab for preliminary test in 2008. Even though the Borgoforte BLESS is longer and wider compared to this one, the basic concepts are the same. The Borgoforte BLESS is long 25 meters and has 34 FBGs. Such sensors measure the temperature at different heights, from 12 m asl to – 17 m asl (10 m asl can be considered a river bed average level). Figure 1.8 shows a sketch of the layout on the left, and a photo of the Borgoforte installation on the A B Cright. BLESS (in black) is fixed to a stainless steel tube fixed downstream of the pier avoiding debris impact. Only the fiber portion hosting the sensors is in contact with the environment, the rest is sheltered.
Fig. 1.8 BLESS system layout (left). Borgoforte case: BLESS is located on a tube close to the pier (right)
The generic FBG measures the temperature of its environment (Tsoil or Twater) when the power unit (that is connected to the electrical circuits) is switched off, from t0 to tstart (Fig. 1.9 left). At tstart, electrical power is turned on until tend, where the FBG registers a temperature increment (Tend soil or Tend water). The temperature variation is defined as ΔT = Tend – Tstart (soil or water). Considering that the heat dispersion in flowing water (by convection) is much higher when compared to that of sensors buried in saturated ground (conduction), the FBGs in flowing water should sense a lower temperature increase than those buried in the river bed; In other words, ΔTwater is less than ΔTsoil.
This clearly appears by deleting the different environmental offset temperatures at tstart, Figure 1.9 right. Looking Figure 1.8 left, the first “n” sensors from the top register a lower ΔT increment when compared to the bottom sensors buried in the river bed. If we analyze the ΔT value of two consecutive sensors, two scenarios might occur. The two sensors are in the same environment (water or ground) so they have about the same ΔT. Otherwise, the two close sensors are in different environments so they have a different ΔT. The river bed level is located between the two consecutive FBGs having a different behavior. By knowing the position of each sensor, we can define a range in which the river bed is located. The uncertainty is equal to the sensor spacing (a chosen parameter). A detailed description of BLESS and a second installation on the same principle can be found in two different papers[21,29].
‐ 19 ‐
Fig. 1.9 Sketch of BLESS acquisition within definitions of main parameters
Fig. 1.10 Cross‐check between BLESS and the echo sounder (the bed level given by the latter is the horizontal black line). Black diamonds and circles define levels in which the river bed level is located Figure 1.10 shows the first check performed after installation (June 2011). For a better visualization, only first 14 sensors are plotted over the 34 available. Two profiles representing two different data analyses are given in the graph: the temperature profile T (when BLESS is used as a thermometer, heating off) and the temperature variation profile ΔT (ΔT = Tend – Tstart, Figure 1.9).
Looking the temperature profile T, the first 3 sensors (from the top) are in flowing water because they measure about the same high temperature. Sensors 7 to the bottom are buried in the river bed because FBG 1 1 2 3 4 5 6 7 3 4 6 5 FBG 2 FBG 3 FBG 4 FBG 5 FBG 6 FBG 7 FBG 8 FBG 9 FBG 10 FBG 11 FBG 12 FBG 13 FBG 14
they have a stable lower temperature. The bed level is in the middle between sensors 3 and 7 (black circles). Unfortunately, this interval is around 2 meters. The temperature decreases without any abrupt variation. It is impossible to accurately establish where FBG 4, 5 and 6 are located, water or soil. This approach (using the temperature) has an intrinsic high uncertainty. Referring to the temperature variation profile (ΔT), we can observe that the first 3 sensors are in the stream. The flowing water efficiently takes away generated heat so they register a low ΔT increment. The others, having a different behavior, are immersed in the ground, from the 4th to the end. In this case there is a “jump” between sensors 3 and 4 (black diamonds). Between them, in a 50 cm interval, we can place the bed level. In addition, the echo sounder measurement is plotted to validate the BLESS output. In both cases, T and ΔT profiles, BLESS defines the bed level range, as validated by the echo sounder which shows better resolution. The temperature profile has two crucial matters. First, the range can be too coarse: 2 meters, in this case, is the estimated uncertainty. Second, the temperature is affected by climate conditions, day/night and seasonal changes. In some periods it was estimated that the temperature of both water and ground are the same, if no heating is applied. For those reasons we use BLESS measuring temperature variation ΔT: it is an even stronger indicator because it is insensitive to seasonal conditions. Moreover, sensor resolution is equal to the FBG spacing along the fiber (in this case the adopted spatial resolution is 0.5 m). The choice of the temperature sensor spacing depends on many factors, strictly correlated with the specific bridge, river and bed characteristics.
1.4 References
[6] B. W. Melville, S.E. Coleman, (2000) Bridge scour. Water Resources Publications, LLC, Highlands Ranch, Colorado, USA. [11] L.A. Arneson, L.W. Zevenbergen, P.F. Lagasse, P.E. Clopper, (2012) Evaluating scour at bridges. Fifth Edition", FHWA‐HIF‐12‐003 ‐ HEC‐18, FHA Washington D.C. [16] Provincia di Mantova (2008), Studio della vulnerabilità idraulica ponte di Borgoforte sul fiume Po. [17] National Cooperative Highway Research Program NCHRP (2009) Synthesis 396: Monitoring Scour Critical Bridges. Project Number: 20‐05/Topic 36‐02 doi: 10.17226/22979.
[18] Federal Highway Administration FHWA (2009) Bridge scour and Stream instability
countermeasures: experience, selection, and design guidance. third edition. Publication No. FHWA‐
‐ 21 ‐
[19] M. Farooq, N. Banthia and F. Azhari, (2017) Bridge scour monitoring: challenges and opportunities. 39th International Association for Bridge and Structural Engineering (IABSE), Vancouver, 21‐23 September 2017.
[20] W. Chen, Y. Xiong, L. Fayun, (2017) A review of bridge scour: mechanism, estimation, monitoring
and countermeasures. Natural Hazards 87:1881–1906 DOI 10.1007/s11069‐017‐2842‐2.
[21] S. Manzoni, G. Crotti, F. Ballio, A. Cigada, F. Inzoli and E. Colombo, (2011) Bless: A fiber optic
sedimeter. Flow Measurement and Instrumentation, doi: 10.1016/j.flowmeasinst.2011.06.010.
[22] A. Cigada, F. Ballio and F. Inzoli, (2008) Hydraulic Monitoring Unit, application for international
patent n. PCT/EP2008/059075.
[23] ESDU 82026 (2002), Strong winds in the atmospheric boundary layer. Part 1: hourly‐mean wind
speeds. ISBN: 978 0 85679 407 0.
[24] F. Ballio, A. Cigada, G. Crotti and S. Manzoni, (2007) Sviluppo di un impianto di monitoraggio
dell’erosione attorno alle pile del ponte ferroviario sul fiume Po in località Borgoforte. Report RFI.
[25] P.A. Johnson, P.E. Clopper, L.W. Zevenbergen, P.F. Lagasse, (2015) Quantifying uncertainty and
reliability in bridge scour estimations. Journal of Hydraulic Engineering 141:04015013.
doi:10.1061/(ASCE)HY.1943‐7900.0001017. [26] B. Mazzorana, J. Hübl, A. Zischg et al., (2011) Modelling woody material transport and deposition in Alpine rivers. Natural Hazards 56: 425‐449. https://doi.org/10.1007/s11069‐009‐9492‐y. [27] S. Franzetti, A. Radice, M. Rabitti, G. Rossi, (2011) Hydraulic design and preliminary performance evaluation of countermeasure against debris accumulation and resulting local pier scour on river Po in Italy. Journal of Hydraulic Engineering, Vol. 137, n. 5, 615‐620. [28] K.O. Hill, G. Meltz, (1997) Fiber Bragg grating technology fundamentals and overview. Journal of Lightwave Technology 15(8) 1263‐1276. [29] G. Crotti, D. Isidori, A. Cigada, F. Ballio, F. Inzoli, E. Concettoni and D. Cristalli, (2016) A hydraulic
monitoring system on a bridge over the River Esino, Italy. Journal of Civil Structural Health
Monitoring, 6(3), 377‐384 doi:10.1007/s13349‐016‐0179‐2.
Chapter 2:
Data analysis
The analysis of the data from all the devices is composed by three different but correlated blocks. Data reliability. One needs to find a procedure to ensure that the measured value (instantaneous or averaged) is representative of the physical quantity that we are measuring. This is something that we can omit for the standard devices (anemometer, hydrometer and video camera) because they can be considered reliable by definition. A device can be classified as standard (Chapter 1) if it is used to measure exactly what it was built for and its installation follows a provided user guide.
Data validation. Even though the data reliability is not an issue, a validation procedure is however necessary to guarantee the proper functioning of the device. Metrology states the need for a periodic confirmation of instrumentation performance against another reference device whose uncertainty is known. This is possible only in a calibration center, but requires uninstalling the device from its position and, unavoidably, stopping the measurements with a loss of data. This is not easily feasible for the present installation, both from the economic and technical points of view. For this reason, we have tried different methods to control the instrumentation’s good performance: doubling the same measurement by means of different sensors and/or using the same data with different calculation approaches. Obviously, this is not an approach to avoid calibration but an economic way to understand if “everything is ok”. Long period analysis. It is carried out over time records longer than a one‐year period. These evaluations allow to understand the behavior of the whole system around the bridge over time. The purpose is to optimize the entire monitoring system and to have a big data archive to use, if necessary.
2.1 Anemometer
Data validation The only way to check the wind data (in every moment) is by comparison against other wind stations located in the bridge surroundings. Figure 2.1 gives this comparison for the cup anemometer on the bridge (black diamonds) and a reference (grey triangles) from a station forming part of the Weather Underground website[30]. The mentioned website collects data from weather stations all around the world. The selected station for a proper data check is called IMANTOVA5 in Rivalta sul Mincio, a small town 19 km north of the bridge. The database collects the average wind speed every 5 minutes, without recording wind gust values. The wind direction is given on a 16‐wind compass rose. Also, real‐time data checks are made possible through this approach, because the website updates frequently.‐ 23 ‐
Fig. 2.1 Comparison between the Borgoforte and IMANTOVA5 anemometer
Looking at Figure 2.1, the wind trends are similar and the measured values are compatible, one in the fluctuation range of the other. The Borgoforte station is exposed to free stream over the river water, thus having quite a different boundary layer when compared to the weather station IMANTOVA5 placed in a residential zone a few meters above the ground (around 10 m). It is expected that any bad working condition of the weather station might translate into a complete lack of signals, or signals uncorrelated with those from nearby stations. Otherwise, signals are similar in shape to those from other weather stations but with a scaling ratio changing with time. This case happens, for instance, due to a growing friction of the ball bearings supporting the rotating vanes. This check is being performed at least twice a year or in the event that strange signals are recorded. The use of a calibrated portable wind station on the bridge is another possibility to perform data checks. Even though this is the preferred choice, as the reference measurement is closer to the sensor being calibrated, this operation is hardly being carried out during critical events such as floods. Long period analysis A long recording allowed for the identification of the main wind directions which are quite important for the purpose of an overall bridge risk evaluation. The bridge is laid south to north whereas the water stream flows from west to east (see Figure 1.3). Overlapping the yearly probability density functions (PDF) of the wind direction, Figure 2.2 PDF ‐ Wind direction, we discover that the main winds blow in the same or in the opposite direction with respect to the water stream. In other words, wind speed produces the highest dynamic pressures on the bridge deck in almost all cases. 0 10 20 30 0 2 4 6 8 10 12 14 16 18 20 22 24 Spee d (km/h) Hours
March 3rd, 2014 - Wind speed Borgoforte IMANTOVA5
0 90 180 270 360 0 2 4 6 8 10 12 14 16 18 20 22 24 D irec tion fr om (°) Hours
Other information can be achieved by the probability density functions (PDFs) of the average wind speed and gusts (over 10 min records), as seen in Figure 2.2. In these plots we can observe that the main statistical wind features are the same, independent of the year. In general, the mean wind speed is low and 99% of velocity values are lower than 10 m/s. A similar scenario applies to the wind gust but values are higher, with a peak seen at the speed of 4 m/s and the 99th percentile at 15 m/s. Fig. 2.2 PDF of wind speed and gust (top). PDF of wind direction (bottom) In 2014 the wind gust PDF has its main peak at lower values even though the 10‐minute average wind speed PDF is aligned with those recorded in other years. This shift can be generated by a growing friction in the ball bearings supporting the rotating vanes, that can explain the odd value of wind gust recorded. A deeper examination comparing data with those from other stations has showed that 2014 generally had lower wind speeds than usual (we exclude a device malfunction). For this reason, keeping track of past data can be a winning point to improve the system’s reliability. Moreover, during the analysis of wind data, we observed that the bridge can alter the wind structure as a function of wind direction. Referring back to Figure 1.3, the anemometer is fixed to the East side of the bridge. So if the wind comes from the East it meets, at the same time, the device and the bridge structure. Therefore, when the wind comes from the West, it meets first the bridge and then the anemometer. An example is plotted in Figure 2.3 where the coefficient of variation (ratio between the standard deviation and the speed average) is shown for all 2013 registered values in the two opposite wind directions. In Figure 1.3, 90° is the angle that represents the East direction; in the Figure 2.3 “from 0 0.002 0.004 0.006 0.008 0 90 180 270 360 PDF - Wind Direction 2013 2014 2012 2011 (N) (E) (S) (W)
‐ 25 ‐ (between 260° and 280°). Looking Figure 2.3 we observe that i) wind speed from the East is higher than that from the West; ii) the relative standard deviation for West winds is twice (0.2) the value for East winds (0.1). Thus, we can conclude that one anemometer is probably not enough for this type of bridge. However, it would be necessary to avoid any bridge influence only if the risk analysis pointed out a real danger in case of improperly‐measured wind speed. Fig. 2.3 Relative standard deviation for all of 2013 from the East (left), from the West (right) and the PDF of wind speed from both directions East and West Obviously, these are only a few of the observations that can be done with this historical database. In the present work we try to explore some statistical analysis to show the potentiality of this approach.
2.2 Hydrometer
Data validation As the monitoring is carried out over the biggest Italian river, it is easy to check this value. Data are available on the internet[31] from the Interregional Agency for the river Po (AIPo) which has its own water level sensors on the same bridge.Figure 2.4 shows an example of the information that can be acquired. In the background there is a portion of the Po basin in which there are highlighted, using triangles, all the water level stations.
0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 12m/s14 Coefficient of variation 2013 -from East 0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 12 14 m/s Coefficient of variation 2013 -from West 0 0.1 0.2 0.3 0.4 0 5 10 15 m/s PDF - Wind Speed From EAST From WEST
Different colors represent different water levels compared to specific AIPo thresholds (green = normalcy, yellow = warning and orange = alert). Fig. 2.4 Water level at Borgoforte using the AIPO webpage (in Italian) Figure 2.5 (hourly average) shows an example in which the two sensors on the bridge are compared for one month, producing exactly the same output. 12 16 20 24 0 3 6 9 12 15 18 21 24 27 30 m asl days
‐ 27 ‐ Long period analysis
Figure 2.6 shows water levels between 2011 and 2015. These long recordings made it possible to obtain some river characteristics. For instance, the periods in which the main floods occur (May and November) or the dry periods with low water levels (July and/or August). These evaluations are also crucial to define maintenance time‐windows. By knowing the level of the echo sounder (12.33 m asl), we can check whether the device is out of the water (June 2012). In this period, a visual inspection can be carried out, if needed. Using a rating curve (Table 1.1), it is also possible to convert the level into the flow rate and vice versa, as shown in Figure 2.7. Fig. 2.6 Borgoforte water levels (from 2011 to 2015) measured by hydrometer Fig. 2.7 Borgoforte corresponding discharges from 2011 to 2015
2.3 Echo sounder
Data reliabilityFor the echo sounder the data reliability check is necessary because we are using the instrument out of its field application, that would be open water, as it is close to the pier where its signal can be disturbed by turbulence, air bubble or suspended transport[17,18]. Bed elevation data are recorded at every second: any single 1‐s reading may be returned in different formats, as follows:
Rxx.xx: the letter R before a number “xx.xx” means that the measured value passed all the internal checks of the validation software. This kind of value is called, henceforth, “Good”; Rxx.xxE: the letter E after a number means that the value did not fully pass the validation checks, thus the user may decide to use or discard it. This kind of value will be called “Unsure”; R99.99E: No echo was received back by the instrument, indicating that the hydraulic condition was too harsh or the device was above the water level; E1: the echo‐sounder did not work correctly. Occasionally, values of type “xx.xx” without any code were recorded. These cases were supposed to be due to an imperfect synchronization between the sonar output and the control software. However, the number of this fake results was fortunately limited. A global analysis of the data for the period 2013‐2015 showed that the Unsure values were generally similar to the Good ones, thus both Good and Unsure data will be used in the analysis. The data treatment presented in the following considers the first 10 days of January 2014. The data are organized in one‐hour file, that contains up to 3600 values. The preliminary operation, not shows in the present work, takes the one‐hour file sent from the Borgoforte monitoring system and removes every single 1‐s reading different from Good or Unsure. The simplest algorithm to compute one‐hour value for the bed elevation is by calculating the mean and the variance of the available data (that are up to 3600 in number). The obtained results are plotted in the Figure 2.8, where with black and red lines are represented, respectively, the bed elevation (one‐ hour mean) and the corresponding variance (using different y‐axis scale). The time of the x‐axis started from 0 (1st January 2014) to 240 hours (10th January 2014). The mean elevation values were stable at around 11 – 11.5 m asl except for times from 105 to 123 hours. In this 20‐hour period, an erosion and deposition process apparently took place. Furthermore, the variance of the values, that was generally negligible (a lower variance would correspond to a more reliable measurement), presented some spikes in the same period (light blue zone).
