Novel image analysis approach to the terrestrial LiDAR monitoringof damage in rubble mound breakwaters

Iván Puente a,n, José Sande b, Higinio González-Jorge a, Enrique Peña-González b,Enrique Maciñeira b, Joaquín Martínez-Sánchez a, Pedro Arias a

a Department of Natural Resources and Environmental Engineering, University of Vigo, School of Mining Engineering, Maxwell s/n, 36310 Vigo, Spainb Water and Environmental Engineering Group (GEAMA), University of A Coruña, Campus de Elviña s/n, 15192 A Coruña, Spain

a r t i c l e i n f o

Article history:Received 9 December 2013Accepted 5 September 2014

Keywords:Terrestrial laser scanningBreakwater modellingCubipodRoundheadDamage progressionVertical deformation

a b s t r a c t

Damage produced in the main armour of rubble mound breakwaters due to wave action is progressiveand different steps can be defined the wave action is beginning, while the breakwaters is falling. It isnecessary to know the damage evolution during the service life of the structure in order to evaluatemaintenance activities and adequately manage the breakwater. New possibilities exist now withterrestrial LiDAR systems, which can quickly scan huge data sets with increased levels of spatial detailand resolution in a very efficient manner.

In this paper, the performance of a Faro Focus 3D has been evaluated for detecting short-termchanges in Cubipod armoured breakwater roundheads. The physical model tests were performed at theCITEEC facilities at the University of A Coruña, Spain. From these basic tests, terrestrial LiDAR has provedto be an accurate technique for damage monitoring in breakwaters. Further research is required toestablish whether this approach is applicable to other LiDAR data sets and specifically for data acquiredin real scale breakwaters.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Rubble mound breakwaters are regularly employed to protectimportant coastal areas such as ports, marinas or beaches from theeffects of attacking waves. Many different armour units, asTretrapods, Dolos, Tripods or Cubipods (Vanhoutte, 2009) havebeen developed and used for their construction during the last 50years. Recently, a number of tests were performed to determinethe stability of single-layer armoured breakwaters. Cubipods, oneof the latest armour units in the market (Gómez-Martín andMedina, 2006), showed the most robust results amongst all.Cubipods (Sato Cubípodo, 2013) are cubic blocks with protuber-ances on the faces to prevent face-to-face coupling and increasefriction with the bottom layer. These massive units resist thewaves due to their own weight, benefiting from the advantages oftraditional cubic elements but preventing from self-packing andsettlement. They also differ from bulky units, whose stability isintrinsic to their mechanical interlocking. Recently, two important

breakwaters at the Port of Malaga and the Outer Port of PuntaLangosteira in A Coruña (Spain) have been constructed using thisspecial piece for coastal protection (Corredor et al., 2013).

Several studies to predict armour damage have been describedfairly extensively in the past (Gómez-Martín and Medina, 2006;Lamberti and Tomasicchio, 1997; Medina et al., 1994; Sumer et al.,2005). The existing formulae consider different wave conditions,as regular or irregular waves or other critical factors such as waveperiod, roundhead radius at sea level, porosity, permeability andstorm duration. These formulae (Burcharth et al., 2011; Van Gentand van der Werf, 2010) also require counting the number ofdisplaced units or measuring the area eroded. Classic methods forobtaining these parameters include visual counting proceduresand photo measurements, which can result in tedious and inaccu-rate processes.

A promising technique above the waterline is Light Detectionand Ranging (LiDAR). Recently, there has been increasing interestin the use of this remote-sensing technology for collecting highspatial resolution data in an efficient and accurate manner.Specifically, the application of LiDAR systems, either terrestrial,mobile (Puente et al., 2013) or aerial (Persson et al., 2002), formapping topography has evolved rapidly, being nowadays opera-tional systems widely available from commercial vendors. In fact,these remotely sensed methods have already been applied to

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Ocean Engineering

http://dx.doi.org/10.1016/j.oceaneng.2014.09.0110029-8018/& 2014 Elsevier Ltd. All rights reserved.

n Corresponding author. Tel.: þ34 986 813 499; fax: þ34 986 811 924.E-mail addresses: ipuente@uvigo.es (I. Puente), jose.sande@udc.es (J. Sande),

higiniog@uvigo.es (H. González-Jorge), epena@udc.es (E. Peña-González),emacine@puertocoruna.com (E. Maciñeira),joaquin.martinez@uvigo.es (J. Martínez-Sánchez), parias@uvigo.es (P. Arias).

Ocean Engineering 91 (2014) 273–280

identify fluvial erosion and deposition (Bowen and Waltermire,2002; Marks and Bates, 2000), hydraulic-modelling studies (Haileand Rientjes, 2005) or to conduct condition surveys in breakwaters(Mitchell and Chang, 2011).

The purpose of the present work is to provide a practicalapproach by which the damage progression around emergedbreakwaters can be monitored using a Faro Focus 3D terrestrialLiDAR. This method applies Matlab image processing techniques tocontrol critical displacements. An automatic alarm will be raisedonce the percentage of displacements exceeds a predefined criticalvalue.

2. Methodology

2.1. Experimental setup

The experiments were performed in the laboratory of Centre ofTechnological Innovations in Construction and Civil Engineering(CITEEC) to investigate the stability of a roundhead protected withCubipods during breaking conditions. This laboratory has a wavebasin available with a cross-section of 32 m width and 30 m long,and half of its width was used for the present work (Fig. 1A). Thisstudy involves only unidirectional waves (parallel to the longestside of the basin). In order to avoid reflective effects, threedissipative beaches were arranged at the sides of the basin. Aslope was constructed in front of the model in order to reproducewave shoaling.

The model was placed at a distance of 15.5 m measured at thetoe of the breakwater from the wave generator paddles. It wasmade up of two roundheads with different roundhead radius atsea water level of r¼47 cm and 51 cm.Only the smaller one will beanalysed here (Fig. 1B and C), to present the preliminary results ofthis technique.

These Cubipod single-layer roundheads were separated by atrunk of 4 m long, making a total length of 6.25 m. The slope anglewas cot α¼H/V¼1.5 and its initial armour porosity p¼42%. Toavoid the influence between both roundheads, the model haddissipative elements at the rear. The roundheads were divided in451 sectors, using pieces of different colours to easily observe thebehaviour in each sector. Due to their tendency to self-positioning,it is widely recognized that results obtained in laboratory testingwill be equivalent to those from full-scale breakwaters.

To study the stability of the Cubipod armour units, the test wasgrouped in series of constant Iribarren number (Irb¼6) and waterdepth (h¼35 cm). Long-crested irregular waves were generatedwith significant wave heights increasing progressively fromHs¼8 cm to the breakwater failure at Hs¼11 cm. The wave periodswere divided into steps from Tp¼2.0 s to 2.2 s. Each individual testwas formed by two trains of 500 irregular waves.

2.2. Data acquisition

Terrestrial LiDAR systems (TLS) have been recognized in the lastdecades for their capability to measure geometry (Molinés et al.,2012). Large amounts of points can be measured from one scanposition, with high accuracy and in a short period of time.

In this experiment, a Faro Focus 3D phase-shift scanner wasused (Fig. 2A) (Faro, 2013a). For phase-based systems, the mea-surements are taken continuously resulting in a higher measure-ment frequency compared to pulse-based systems. This fact makesthem more suitable for precise surveys. Technical characteristicsare shown in Table 1.

Scans were performed with a field-of-view of 401 (Hz) by 341 (V),the same scan resolution and scan speed (Fig. 2B). Since samplingefficiency was crucial, scanner parameters were chosen such as notto exceed a scan time of 20 min. A total of seven scans of thebreakwater roundhead were gathered from the same scanner

Fig. 1. (A) Test set-up in the laboratory wave basin. (B) Schematic top view of the breakwater analysed. (C) Picture of the breakwater roundhead protected with a single-layerof Cubipods after testing with a wave height Hs¼9 cm.

I. Puente et al. / Ocean Engineering 91 (2014) 273–280274

location in different wave steps. The first acquisition was done for aninitial wave height Hs¼8 cm. The following trains were againacquired after 500 and 1000 waves for wave heights Hs¼8 cm,9 cm and 10 cm. The breakwater finally broke during the first 500waves for wave height Hs¼11 cm.

Those single scans were subsequently used to analyse thedamage progression, resulting in a merged scan dataset with acommon coordinate system. The point cloud obtained from thespherical coordinates (R, h, u) was then converted directly toCartesian coordinates (X, Y, Z) by the software Scene 4.8 from Faro(2013b).

3. Damage analysis

For the analysis of the damage in the roundhead, we employedthe Faro Focus 3D data. Due to the attenuation of this infrared LiDARin water, the damage of the entire slope from toe to crest cannot bemonitored. In order to check the viability of this technique, westudied only the damage that occurred above the water level.

In addition, as the geometric shape of the emerged and sub-merged parts of the breakwaters is similar, the algorithms andmethodologies developed for the emerged part could be easilytransferred to the submerged part in future. Real breakwater surveyscan obtain the submerged geometry using additional techniques,such as multibeam bathymetry, to create its complete map.

3.1. Automatic colour map generation for realistic volumedisplacements

This section first introduces an algorithm to compute thevertical displacements in the roundhead. The code, written inMatlab language, uses the geometric information provided byLiDAR. All data sets must be co-registered together in the samereference frame and a rectangular grid superimposed over them is

also required. The spatial resolution, which is a user-definedparameter, was set to 0.001 m.

Then, the algorithm makes use of interpolation functions forscattered data (X, Y, and Z) to create a fitted 3D surface for eachdataset. The following step computes the differences in height (dZ)between the initial dataset (wave step 1) and consecutive data setswith the same X, Y grid coordinates. The result is a 2D raster imageof 8 bits, where each pixel gives information about how thepunctual height evolves with time.

For the volume estimation, we multiply each grid area with itscorresponding height. The result will be a 2D image, and the pixelwill inform about the volume displacement for each specific XYposition. Several colour maps, both for vertical and volumedisplacements, will be provided for monitoring purposes. Custo-mizable sets of horizontal (or level curves) and transversalsections can also be generated.

3.2. LiDAR image analysis for damage progression

Following the previous approach, this second algorithm putsforward the application of Matlab image processing techniques tocreate a damage monitoring system in breakwaters. The percen-tage of the critical damages on the surface of the roundhead can beobtained by using some functions in the image processing toolboxof Matlab, such as filtering, binarization and so on. The automaticalarm will be raised when the percentage of these damagesexceeds a threshold value. Damage progression in breakwaterroundheads can be controlled by this procedure effectively.

The flow chart is given in Fig. 3, and a summary of these steps islisted below using the point cloud data collected during wave step 3.

3.2.1. ThresholdingThe vertical displacements are the key information carrier.

Therefore, a rule is set to define when a displacement is signifi-cant. Authors assume that there is no possible movement betweenthe first two wave steps, so the standard deviation for dZ12 isconsidered to be the precision of the technique (Gaussian prob-ability distribution; confidence level: 68%) and includes registra-tion errors, laser ranging errors, angular errors, etc. The detectedpotential damages are classified according to a 95.5% confidencelevel. The 2σ criterion was used, since the Faro system is veryaccurate and authors want to avoid providing false positive alerts.

Therefore, the general criterion for the alert is: if the absolutevalue of the differences in height for consecutive data sets (dZ13,dZ14, dZ15, dZ16 and dZ17) is greater than 2σ (dZ12), then damageis detected and those areas with damage are highlighted red.Otherwise, there is no alert.

Fig. 2. (A) Faro Focus 3D during the data acquisition. (B) View of a Faro Focus 3D point cloud of the emerged roundhead.

Table 1Technical data of terrestrial laser scanner FaroFocus 3D according to manufacturer datasheet.

Measurement range o120 mMinimum range 40.6 mRepeatability (25 m, 10% refl.) 2.2 mmMeasurement rate 976,000 pts/sLaser wavelength 905 nmBeam divergence 0.19 mradVertical field of view 0–3051Step size (vertical/horizontal) 0.0091Horizontal field of view 0–3601

I. Puente et al. / Ocean Engineering 91 (2014) 273–280 275

3.2.2. Filtering and image binarizationThe thresholded image (Fig. 4A) contains isolated points,

considered as noise, which needs to be filtered away. Applying amedian filtering of the original matrix in 2D, the result can be seenin Fig. 4B. The same filter is used in all images. Within this step,image binarization occurs simultaneously, converting an image ofup to 256 grey levels to a red and white image.

3.2.3. Inversion and centroid calculationAn image inversion is required to determine the centroids of

damaged areas. Original binary images (Fig. 4B) are transformedinto a work version image with damages plotted in white colour

against a black background. Applying some mathematical mor-phology functions, centroid coordinates and projected areas foreach individual damage in the binary image can be measured.Only those centroids, corresponding to areas of damage biggerthan 7 cm2, are saved (Fig. 5A). It should be noted that this limitwas set considering the size of the Cubipods but it can be adjustedto specific requirements.

The origin of the image coordinate system is in the top-leftcorner at point (1, 1). As we know the real coordinates of the samepoint (x¼�7.2812 m, y¼�0.1804 m) measured from the scanner,the aforementioned centroids can be easily referenced to thescanner coordinate system by translating the pixel displacementto distance in meters.

Fig. 3. Flow chart of the image procedure.

Fig. 4. (A)Thresholding step for data collected during wave step 3 and (B) median filtering and binarization steps for data collected during wave step 3. (For interpretation ofthe references to colour in this figure, the reader is referred to the web version of this article.)

I. Puente et al. / Ocean Engineering 91 (2014) 273–280276

The breakwater roundhead can be regarded as a surface areawith a colour distribution given by the image intensities, stored inan 8-bit integer (Fig. 5B). Damages are finally superimposed in thegrayscale image by looking at those pixels in Fig. 4B that representdamage and substituting their values in the intensity image. Theresult is shown is Fig. 5B.

3.2.4. Calculation of damaged areas ,volumes and numberof Cubipods

Damages are assigned the value ‘0’ during the image binariza-tion process (Fig. 4B) or the value ‘1’ after the inversion step(Fig. 5A). Counting the number of pixels with value zero or oneand knowing the pixel dimensions, the total area of damage can becomputed for each wave step. Moreover, an estimation ofdamaged volumes in each wave step can be obtained by multi-plying each damaged area with its corresponding height difference(dZ1�N), where N ranges from 3 to 7. On the other hand, as theporosity has been calculated for each wave step, the number ofdamaged units can also be derived using the following formula:

p¼ 1�nSnSt

ð1Þ

where p represents the porosity, n is the number of damagedunits, Sn is the nominal surface of the Cubipod (14.59 cm2) and Stthe total damaged area for each wave step.

4. Results and discussion

Several test series were performed in the roundhead with thewave height Hs being in the range 8–11 cm. These tests showedthat there is one highlighted segment with greater verticaland volume displacements, namely the “rear-side segment” (i.e.,a segment with relatively low resistance) starting at an angle of901 relative to the wave direction. As expected, these displace-ments were unevenly distributed over the segments. Previously,Lomónaco et al. (2009) proved that the maximum damage wasobserved at angles between 901 and 1351 from the incident wavedirection.

Fig. 6A clearly illustrates the fact stated above. The colour mapdemonstrates the height differences between wave steps 1 and7 as a result of the wave trains executed for this experiment. Twowave steps correspond to each Hs, except for the wave interval7 that corresponds to only 500 waves of Hs¼11 cm.

Fig. 6B represents the breakwater collapse after wave step 7. Asexplained in Section 3.1, and here exemplified for wave train 7, thismethod can also be applied for calculating the volume displace-ments in the breakwater, multiplying each pixel area (10�6 m2)with the corresponding height of each grid cell.

In addition, customizable sets of horizontal and transversalsections were generated for monitoring purposes. Fig. 6A includesfour level curves at Z2¼�0.799 m, Z4¼�0.734 m, Z6¼�0.668 mand Z8¼�0.603 m, where Z¼0 m is located in the scanner.

Fig. 7 illustrates the vertical deformation measurements suf-fered in the roundhead after seven different wave steps. As seen,the profiles were measured parallel to the y-axis for x¼�6.861 m(see Fig. 6A). It is possible to identify significant changes afterwave step 7.

Fig. 8A shows the development over time of the damages.Following the work procedure explained in Section 3.2, imageanalysis is done after a series of LiDAR images are generated usingMatlab software. The evaluation of repeated LiDAR images on thesame breakwater provided both a fast and automated approach ofmonitoring damages.

For each wave step, authors computed the damages, their areasand the Cartesian coordinates of those centroids corresponding toareas of damage bigger than 7 cm2, using the method explained inSection 3.2. Additionally, the centroid sizes are weighted withrespect to the area they represent such that the bigger the area,the bigger the centroid is plotted (Fig. 8B). This helps users tocreate a more reliable overview of the damage progression. Withthis strategy, the reparations of the breakwaters could be mademore continuously in time and before a potential undesirablefailure.

Fig. 8B does not include the centroid coordinates just as a stylematter but it could definitely be possible, as explained in Section 3.2.3.

For the quality evaluation of the results obtained here andclassified the pixels as damaged (in red) computed by the algorithm

Fig. 5. (A) Inversion and centroid calculation with pixel coordinates; and (B) areas of damage in red superimposed on the intensity image and Cartesian coordinates ofcentroids. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

I. Puente et al. / Ocean Engineering 91 (2014) 273–280 277

(Fig. 9B) with those manually marked by an expert in breakwaters(Fig. 9A).

A grid resolution of 76�59 pixels was superimposed to bothimages and an example is given for the last wave step 7. Greenpixels correspond to areas where the algorithm has properlyclassified the pixels as damages. White pixels represent the falsepositives while yellow pixels are the missed damages.

An error of commission, sometimes also called as “falsepositives”, occurs when the algorithm erroneously included apixel of “damage” for consideration. The Commission Error, Ce, isthe percentage of pixels classified as damage which do not belongto that class according to the reference data (commission). For itsestimation, authors computed the ratio of false positives to realdamages, as follows:

Ce %ð Þ ¼ 135=544� 100¼ 24:81% ð2Þ

An error of omission occurs when damage has been left out bythe algorithm. The omission error, Oe, is the percentage of thepixels, belonging to class “damage” in the reference data, whichhave not been classified as such.

When actual damage was not identified by the algorithm andthe ratio between missed and real damages is

Oe %ð Þ ¼ 9=544� 100¼ 1:65 % ð3Þ

The accuracy of the method can be evaluated using thefollowing formula:

Accuracy ACCð Þ ¼ 1� ∑False positiveþ∑False negative� �

∑Total population

Accuracy ACCð Þ ¼ 1� 135þ9ð Þ2722

¼ 1�0:052¼ 0:947 ð4Þ

The precision or positive predictive value (PPV) is computed asfollows (Eq. 5):

Precision PPVð Þ ¼ ∑True positive∑True positiveþ∑False positive� �

Precision PPVð Þ ¼ 544544þ135ð Þ ¼

544679

¼ 0:801 ð5Þ

Considering the omission and commission errors, authorsevaluated the selected 2σ criterion as good for this case study.

The omission errors are close to zero, representing an idealsituation because only small changes in the breakwater will bemissed (1.73%). On the other hand, the 2σ approach overestimatesthe margin of error (safety factor) resulting in a wider interval ofdamages. Consequently, the commission errors will be high(24.81%) though, this approach will provide a margin over thetheoretical damages.

Fig. 7. Vertical deformation measurements at x¼�6.861 m.

Fig. 6. Colormap representing the height differences between wave trains 1 and 7 in the roundhead and level curves Z2, Z4, Z6 and Z8. (B) Roundhead picture after wavestep 7.

I. Puente et al. / Ocean Engineering 91 (2014) 273–280278

Given that the projected roundhead area, emerged above thewater level, is approximately 0.438 m2, it has been possible tomonitor the percentage of the critical damages over time.

Moreover, the number of damaged units can also be compu-ted using the formula (1). Knowing the porosity for wave steps 4(p¼0.395, Hs¼9 cm) and 6 (p¼0.44, Hs¼10 cm), and theircorresponding damaged area, the number of units damaged

is directly derived. Table 2 summarizes the results from thistest.

It should be noted that the roundhead area varies slightly fromwave step to wave step because of the Cubipods’ movement andreplacement and also the increase of the wave height.

The evolution of damage in the test has been very constant(see Fig. 8A and Table 2). It starts in the submerged zone of the

Fig. 9. (A) Damages identified by an expert. (B) Damages classified by the algorithm, with false positives (white pixels) and missed damages (yellow pixels) also represented.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 8. (A) Damage progression in the breakwater roundhead. (B) Weighted centroid locations for the most significant damages over time.

I. Puente et al. / Ocean Engineering 91 (2014) 273–280 279

breakwater due to the direct influence of the waves. After wavestep 3, displacements were evaluated and because of that, theCubipods located immediately next to those pieces, are alsomoving into the holes left by the units removed. The same patternis repeated in the emerged zone during the following wave steps,as the units keep moving downwards. The failure will occur in theemerged part of the breakwater relatively soon after the firstincrease in porosity.

The rigid behaviour of the breakwater can be observed duringthe evolution of the damaged area measured. Between wave steps6 and 7, the damage will grow significantly and it will produce thefailure with only an increase of 1 cm in the waves.

These results show the applicability of this technique (at thispoint in the laboratory) as a powerful tool to improve a main-tenance strategy for an authority port. After some storm eventsand a revision of the breakwater profiles, this methodology canshow the potential weak points in the structure in intermediatewave steps, checking the centroids affected in the active zone.Actual tendencies to operate in repairing breakwaters have herean interesting tool to anticipate and prevent increasing erodedareas and final failure of breakwaters.

5. Conclusions

Within this paper, the suitability of terrestrial LiDAR for short-term monitoring of damages in breakwaters has been assessed.Both vertical and volume displacements were also evaluatedthanks to the phase-based LiDAR capability to provide largeamounts of accurate points from the breakwater roundhead in ashort acquisition time span. Tests were performed in a small-scalemodel, made of Cubipods, in the CITEEC facilities.

Surfaces sampled in seven different wave steps were analysedfor vertical deformations using the algorithm explained in Section3.1. Punctual volume deformations or level curves can also becomputed for monitoring purposes.

In Section 3.2, a damage-growth monitoring procedure hasbeen developed using some Matlab image processing techniques.The automated image analysis will minimize the manual interac-tions from the maintenance personnel. The results achieved fromthis study are useful for structural monitoring applications andmaintenance strategies. The method makes it possible to referencethe damages and check their evolution over time.

The methodologies used for applications in the laboratorymight also be applied to full-scale breakwaters, using long rangelaser scanners or even mobile LiDAR systems that could bedeployed on boats (Alho et al., 2009; Mitchell and Chang, 2011),and multibeam echo sounders to measure the submerged area

(Dix et al., 2012). Automation has been improved but further workis necessary to adapt both methodologies in real environments.

Acknowledgements

Authors want to give thanks to the Spanish Ministry of Economyand Competitiveness and Xunta de Galicia for the financial supportgiven; Human Resources programs (BES-2010-034106 and IPP055 –

EXP44) and project (Grant no. EM2013/005).

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Table 2Damaged area over time.

Damagedarea (m2)

Breakwaterarea (m2)

Damagedvolume (m3)

Damagedarea (%)

Numberof unitsdamaged

Wave step 3 0.0201 0.4357 9.1403*10�4 4.613 –

Wave step 4 0.0192 0.4377 9.7934*10�4 4.387 8Wave step 5 0.0242 0.4380 0.0011 5.525 –

Wave step 6 0.0381 0.4387 0.0014 8.685 15Wave step 7 0.0963 0.4397 0.0036 21.901 –

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