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A Review of the Use of Terrestrial Laser Scanning Application for
Change Detection and Deformation Monitoring of Structures
Wallace Mukupa*, Gethin W. Roberts, Craig M. Hancock, Khalil Al-Manasir
Department of Civil Engineering, The University of Nottingham Ningbo, China
199 Taikang East Road, University Park, Ningbo 315100, China.
*Corresponding Author. Email: wallace.mukupa@nottingham.edu.cn
Abstract
Change detection and deformation monitoring is an active area of research within the field of engineering
surveying as well as overlapping areas such as structural and civil engineering. The application of
Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete
structures has increased over the years as illustrated in the past studies. This paper presents a review of
literature on TLS application in the monitoring of structures and discusses registration and georeferencing
of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS
research work has shown some trends in addressing issues such as accurate registration and georeferencing
of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud
processing techniques for deformation analysis, scanner calibration issues and assessing the potential of
TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to
investigation as far as TLS is concerned in change detection and deformation monitoring studies such as
rigorous and efficient workflow methodology of point cloud processing for change detection and
deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise
structures, design of data acquisition and quality assessment for precise measurements and modelling the
environmental effects on the performance of laser scanning. Even though some studies have attempted to
address these issues, some gaps exist as information is still limited. Some methods reviewed in the case
studies have been applied in landslide monitoring and they seem promising to be applied in engineering
surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis
is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are
being developed necessitating more research for precise measurements in the monitoring of structures.
Keywords: Terrestrial laser scanning, change detection, deformation monitoring, registration, georeferencing, review
1.0 Introduction
Geodetic monitoring of structures is a common practice in the field of engineering surveying
(Mechelke et al., 2013). Monitoring structural deformation is one of the major concerns when
dealing with structures such as bridges, tunnels, dams and tall buildings (Han et al., 2013). These
engineering structures are examples of structures that are routinely surveyed and monitored for
their stability as they are subject to deformation due to factors such as changes of ground water
level, tidal phenomena, tectonic phenomena, etc. (Erol et al., 2004). Periodic monitoring of the
structural response is necessary to rationally secure and maintain the safety of structures (Park et
al., 2007). The knowledge about types, characteristics and scales of structural deformations is
essential when defining their nature and for the consequent verification of potential permanent
damage possibilities or eventual destruction of structures (Vezocnik et al., 2009). Deformation
monitoring of the static and dynamic behaviour of engineering structures is an active area of study
due to the impact that these structures have on the landscape where they have been built and the
potential damage they can cause in case of a malfunction (Gumus et al., 2013; Schneider, 2006).
Within the field of engineering surveying, especially in the area of deformation and displacement
measurement of structures, traditional point-wise surveying methods are mostly used. Vertical
displacements and elevations are measured by high precision levelling whilst the spatial
displacements and movements are derived by total stations or theodolites (Lovas et al., 2008).
From the past to present, several studies have been carried out on the monitoring of engineering
structures and different geodetic techniques have been applied to monitor structures. There exists a
vast literature on monitoring of structures and detection of deformations using geodetic and
geotechnical techniques (Chrzanowski et al., 2011). Deformation monitoring using TLS is gaining
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attention due to the high point density and spatial resolution that can be acquired in a short time, but
the use of laser scanning for deformation monitoring is still in its infancy (Walton et al., 2014).
Driven by progress in sensor technology, computer software and data processing capabilities, TLS
has recently proved a revolutionary technique for high accuracy 3D mapping and documentation of
physical scenarios (Gikas, 2012) and a wide range of other application fields involving change
detection and deformation monitoring of structures (Vezocnik et al., 2009; Lindenbergh et al., 2009;
Scaioni, 2012). The application of TLS surveying technique is broadening in the civil engineering
industry for example Lovas et al. (2008). TLS has also seen some developments in, for instance,
more methods being available to characterize the quality of acquired data and it has become more
and more known as a surveying technique to a wider audience (Lindenbergh, 2013). The principle
of laser scanning technology in terms of operation and application has been presented by several
authors (Lichti et al., 2002a; Staiger, 2003; Reshetyuk, 2009; Shan and Toth, 2009; Abbas et al.,
2013). TLS has received attention because it offers numerous measurement benefits ranging from
direct 3D data capture from a single instrument setup, remote and noncontact (targetless) operation,
and dense data acquisition (Gordon and Lichti, 2007). The growing importance of this technology
is also mirrored by the establishment of FIG Taskforce 6.1.5 on ‘Terrestrial Laser Scanning for
Deformation Monitoring’ and ISPRS Working Group V/3 on ‘Terrestrial Laser scanning’ (Tsakiri
et al., 2006). This paper reviews the trends in the application of TLS in change detection and
deformation monitoring and proposes a hybrid three-stage process of deformation analysis of laser
scanning data based on the existing techniques. A discussion on registration and georeferencing in
TLS is also presented as these initial data processing steps are essential prior to 3D modelling or
analysis in applications such as change detection and deformation monitoring. Furthermore,
Wujanz et al. (2013) state that a critical component within the process chain of deformation
monitoring is the transformation of subsequently captured point clouds into a reference respectively
superior coordinate system.
2.0 Change Detection and Deformation Monitoring
The purpose of change detection is to determine if the geometric state of a scene has changed.
Change detection looks for a binary answer, has a situation changed or not whereas deformation
analysis looks for a quantified change (Lindenbergh and Pietrzyk, 2015). Change detection is the
process of identifying differences in the state of an object or phenomenon by observing it at
different times. Change detection has an old research history and wider applicability in the field of
remote sensing. A variety of change detection techniques have been developed in the remote
sensing field and reviews of their applications and recommendations for selection of suitable
change detection methods have been reported (Lu et al., 2004). However, as TLS has matured as a
surveying technique, various researchers have carried out studies to identify changes in a scene or
on an object's surface from repeated laser scanning surveys. Actually, change detection,
deformation analysis and structural monitoring is different terminology for strongly related topics
and in terms of TLS what they all have in common is that they all compare point clouds of the same
scene or object, but acquired at different epochs (Lindenbergh, 2013).
Deformation monitoring is the systematic measurement and tracking of the alteration in the shape
or dimensions and position of an object as a result of the application of stress to it. Deformation
monitoring is a major component of logging measured values that may be used for further
computation, deformation analysis, predictive maintenance and alarming (Dunnicliff, 1993).
Deformation monitoring has also been defined as the surveying of a region of interest at different
points in time and identifying geometric changes based on the captured data in between the
respective epochs. Furthermore, stating that a stable reference frame is required which is described
by immovable control points and that in order to achieve this prerequisite, deformed regions need to
be identified within the area under investigation (Wujanz et al., 2013). There are several techniques
for measuring deformations. These can be grouped mainly into two as geodetic and geotechnical
techniques. TLS is an example of a geodetic technique (Chrzanowski et al., 2011).
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3.0 Terrestrial Laser Scanning Change Detection
Different point cloud comparison techniques have been used by many researchers to detect change
between sequentially gathered point clouds (Girardeau-Montaut et al., 2005). The cloud to mesh
method has been used with success to investigate structural and surface deformation. Gridded data
sets can be compared to produce a DEM of difference, which highlight areas of loss or
accumulation (Barnhart and Crosby, 2013). Many authors have actually reported methods
implemented for surface modelling of a deforming object that involve simple gridding (Tsakiri et
al., 2006). However, isolating regions of interest and creating DEMs or surface models is time
consuming and requires interpolation. Furthermore, a topographic data reduction occurs when point
cloud data is reduced from 3D to 2.5D. Monserrat and Crosetto (2008) also stated that some authors
have performed deformation studies by directly comparing DEMs from different TLS campaigns.
Though this approach is easy to implement using commercial software, general purpose algorithms
are often not sufficient for specific purposes such as change detection. For instance it is not
appropriate to use the built-in PolyWorks software functions for calculation of shortest distance as
the result is normally an underestimation of the total displacement or change (Walton et al., 2014).
Furthermore, the approach of comparing DEMs has two important limitations. Firstly, it has limited
sensitivity to small deformations. Secondly, since the DEMs are typically defined on a 2D support,
i.e. z=f(x,y), the difference between DEMs basically provides a 1D deformation measurement in
the z direction. Given the 3D nature of the TLS point clouds, this represents an important limitation
of this approach (Monserrat and Crosetto, 2008).
The cloud to cloud methods have also been used to directly compare point clouds but they often do
not return signed (-/+) displacements, which are required for many applications. Although both
cloud to mesh and rotated DEMs of difference techniques enable comparisons of complex point
cloud scenes, they require a high degree of data processing and isolation to work effectively (Lague
et al., 2013). Cloud to mesh comparisons provide good approximations of surface change, yet they
do not take into account the local orientation of the surfaces represented by the point clouds.
Furthermore, cloud to mesh and DEMs of difference methods are labour and time intensive because
areas of interest must be carefully isolated and treated in the TLS data processing workflow to
create reference meshes and DEMs (Barnhart and Crosby, 2013). Meshing TLS data is not only a
limitation as a second interpolation of the data, which adds a level of error to the data, but meshing
can also create erroneous surfaces (Walton et al., 2014). In view of some of the limitations of the
change detection techniques mentioned above, Lague et al. (2013) present a new change detection
algorithm called Multiscale Model to Model Cloud Comparison (M3C2) which combines for the
first time three key characteristics: it operates directly on point clouds without meshing or gridding,
it computes the local distance between two point clouds along the normal surface direction which
tracks 3D variation in surface orientation and it estimates for each distance measurement a
confidence interval depending on point cloud roughness and registration error. Worth considering
from Lague et al. (2013) are the three main sources of uncertainty in point cloud comparison for
change detection which include:
i. Position uncertainty of point clouds as a result of the instrument used and that the case may
be worse with increase in distance and incidence angle.
ii. Registration uncertainty between the point clouds.
iii. Surface roughness related errors caused by the difficulty to reoccupy exactly the same
scanning position (Alba et al., 2006; Gonzalez Aguilera et al., 2008) between surveys.
In summary, a common technique to perform change detection on TLS point cloud data is to
compute and compare distances between point clouds at different epochs. It can be done either
directly using point cloud data or by creating an intermediary model on top of the points. Cloud to
mesh and mesh to mesh distance measurement techniques have been very well studied and
demonstrated (Girardeau-Montaut et al., 2005) and in order to avoid repetition of the same
information on change detection techniques, the reader of this paper is referred to Lague et al.
(2013). It is also worth noting that in Hancock et al. (2012) a preliminary study was undertaken
aimed at change detection of fire-damaged concrete using TLS intensity data.
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4.0 Terrestrial Laser Scanning and Deformation Monitoring
Deformation monitoring is typically undertaken with point-wise surveying techniques, such as total
station or GNSS (Lovas et al., 2008). However, the capability of TLS to provide high spatial
resolution three-dimensional data with speed and accuracy has considerably gained the attention of
engineers in recent years and TLS has become increasingly used in different engineering surveying
applications such as structural deformation monitoring (Abbas et al., 2013). The motivation is that,
TLS as applied in deformation monitoring offers advantages such as remote measurement and
therefore, the direct object accessibility is not required (Vezocnik et al., 2009). Other advantages of
TLS include a permanent visual record and high spatial data density as does photogrammetry
although the requirement of targets or other sensor compositions installed in the monitoring scene
in minimised (Gordon et al., 2003; Lichti et al., 2002a).
Due to some of the advantages mentioned above, in the last few years there has been an increasing
interest in exploiting TLS data for deformation monitoring. However, when working with TLS data
for quantitative analysis for deformation measurement or change detection, it is not sufficient to
just look at raw point cloud data, post-processing is necessary to generate usable data (Walton et al.,
2014). Actually, the exploitation of the high redundancy provided by TLS tools is key to achieving
good deformation measurement performance with TLS data and often requires the development of
ad hoc tools (Monserrat and Crosetto, 2008). In the same vein other researchers have also stated
that new ways to fully exploit the huge quantity of information provided by TLS point clouds are
still needed (Abellan et al., 2009; Lindenbergh, 2013). Traditionally, deformation analysis studies
are based on displacement data obtained using conventional surveying techniques as mentioned.
These methods can detect millimetre level displacements and they measure the displacements at a
limited number of points (Gikas, 2012).
In contrast to conventional surveying techniques of superior accuracy, the single point precision of
medium to long range TLSs varies from ±2mm to ±25mm, depending on the instrument model and
observation conditions. However, the theoretical precision of a surface derived from spatially dense
point cloud data is substantially higher than the single point precision (Tsakiri et al., 2006; Gordon
and Lichti, 2007). Furthermore, Gordon et al. (2003) state that techniques may be employed to
improve the single point precision of a TLS system. One method involves averaging repeat scan
clouds where multiple scans acquired sequentially are averaged to create a cloud that may be two to
three times more precise than an individual cloud, according to the root of the number of repeat
scans. For example, four repeat scans will theoretically improve the standard deviation of a single
point by a factor of two and that this has been empirically verified by Lichti et al. (2002b).
According to literature there are studies that have examined the potential of TLS in detecting
millimetre deformations with success as reported in the subsequent sections of this paper.
In addition, Monserrat and Crosetto (2008) state that the key idea is to overcome the limited
precision of the single TLS points by taking advantage of the high redundancy of the observations,
i.e. the 3D points and the geometric characteristics of the observed surfaces. An appropriate TLS
data analysis method is one which takes full advantage of the high sampling density of the TLS
data, guarantees a high observation redundancy for the least squares based estimate and hence a
good precision of the estimated deformation parameters (Monserrat and Crosetto, 2008). In a
similar vein Gordon and Lichti (2007) state that although individual sample points are low in
precision and therefore preclude their use in deformation monitoring, modelling the entire point
cloud of high data redundancy leads to a much higher precision of the estimated parameters and an
effective way for representing the change of shape of a structure.
Several case studies applying TLS for deformation monitoring of engineering structures and land
deformation have been carried out in recent years. The deformable objects that have been studied
include different engineering structures such as dams, bridges, tunnels, pipelines, towers, viaducts
and many others structures. Some notable case studies are described below:
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Alba et al. (2006) present some initial results of a project aimed to assess the feasibility of
monitoring deformations of large concrete dams by TLS. The study was focused on two main
problems: the first one was the accuracy and the stability of georeferencing; the second one was the
computation of deformation based on the acquired point clouds. Two approaches are presented for
the analysis of surface displacements, including the shortest distance between the consecutive point
clouds (one being a surface model) and additionally displacements computed by comparing two
regular grids of the dam face. Results showed that the use of the TLS technique can give an
important contribution to the deformation analysis of large dams. The first results obtained from
data processing were dense and accurate maps of deformations of the dam downstream face. Some
maps of deformations derived from comparison of two laser scanned point clouds captured at
different times were produced. Furthermore, it is stated that there is need to improve the accuracy
of scan georeferencing.
One interesting approach for structural monitoring of large dams by TLS is described by Gonzalez
Aguilera et al. (2008). The approach proposed in the study dealt with the application of TLS to the
structural monitoring of a large dam considering aspects related to the need for accuracy control in
georeferencing as pointed out in Alba et al. (2006) together with rigorous approaches to model
complex structures. The novelty of the approach was on the utilization of the Radial Basis Function
(RBF) for the surface parameterisation as well as the incorporation of an original re-Weighted
Extended Orthogonal Procrustes (WEOP) analysis for georeferencing and the accuracy control of
the different measurement periods. It is stated that a TLS sensor alone does not suffice in for
instance providing a control network of large dams. Some challenges involve the impossibility of
scanning the same point in different epochs and the issue of the laser beam width, an observation
which was also made in Lichti and Gordon (2004). The 3D modelling approach via RBF improved
the nominal precision of the TLS and deformation accuracy results in the range of millimetres were
achieved. Accurate georeferencing was achieved by first having a high precision topographic
network in order to define a precise and stable reference frame and then applying the WEOP to the
range dataset (Gonzalez Aguilera et al., 2008).
In Zogg and Ingensand (2008) load tests of a viaduct (concrete span bridge) were performed to
evaluate the fatigue resistance and refine the analytical models. The main objectives of the
deformation monitoring by TLS on the viaduct were on one hand to get to know the advantages and
limits of the measurement technology for load tests in bridge monitoring and on the other hand to
compare TLS with precise levelling in terms of measurement accuracy and detection of
deformations. The deformation analysis of TLS data involved both area-wide and discrete analysis
using the sphere targets. TLS data were recorded in a local system without any connection to the
outside of the viaduct and so local deformations as deflection of the cantilever slab of up to 20mm
under a maximum load of about 100 tons were detected by the area-wide analysis whereas the
target spheres performed relative deformations of up to 6mm. In contrast to TLS, precise levelling
was connected to a transfer point outside the viaduct and absolute deformations of the bridge girder
were detected. For TLS, in order to detect absolute deformations of the bridge girder, the data were
transformed into the reference height system defined by precise levelling. A comparison of the
transformed vertical displacements detected by TLS and the vertical displacements by precise
levelling showed that the deformations of the bridge girder were within the same range. Maximum
differences between the two measurement methods were around 3.5mm. But considering the mean
residuals for the different loading situations, the differences between TLS and precise levelling
were less than 1.0mm. TLS was able to detect deformations in mm range.
Lovas et al. (2008) present a study which deals with the potential of TLS in deformation
measurements involving a bridge load test measurement and a laboratory test. During the controlled
load testing of a full-scale steel portal frame, each load phase was captured by TLS. The
displacements of dedicated points of the structure were also measured by high-precision inductive
transducers for comparison of results from both methods. The structural displacements derived
from the TLS data sets were in strong correlation with those obtained from the traditional
