Rapid, accurate structural health monitoring (SHM) assesses damage to optimise decision-making. Many SHM methods are designed to track nonlinear stiffness changes as damage. However, highly nonlinear pinched hysteretic systems are problematic in SHM. Model-based SHM often fails as any mismatch between model and measured response dynamics leads to significant error. Thus, modelfree methods of hysteresis loop tracking methods have emerged. This study compares the robustness and accuracy in the presence of significant measurement noise of the proven hysteresis loop analysis (HLA) SHM method with 3 emerging model-free methods and 2 further novel adaptations of these methods using a highly nonlinear, 6-story numerical structure to provide a known ground-truth. Mean absolute errors in identifying a known nonlinear stiffness trajectory assessed at four points over two successive ground motion inputs from September 2010 and February 2011 in Christchurch range from 1.71-10.52%. However, the variability is far wider with maximum errors ranging from 3.90-49.72%, where the second largest maximum absolute error was still 19.74%. The lowest mean and maximum absolute errors were for the HLA method. The next best method had mean absolute error of 2.92% and a maximum of 10.51%. These results show the clear superiority of the HLA method over all current emerging model-free methods designed to manage the highly nonlinear pinching responses common in reinforced concrete structures. These results, combined with high robustness and accuracy in scaled and fullscale experimental studies, provide further validation for using HLA for practical implementation.
Following the devastation of the Canterbury earthquake sequence a unique opportunity exists to rebuild and restructure the city of Christchurch, ensuring that its infrastructure is constructed better than before and is innovative. By installing an integrated grid of modern sensor technologies into concrete structures during the rebuild of the Christchurch CBD, the aim is to develop a network of self-monitored ‘digital buildings’. A diverse range of data will be recorded, potentially including parameters such as concrete stresses, strains, thermal deformations, acoustics and the monitoring of corrosion of reinforcement bars. This procedure will allow an on-going complete assessment of the structure’s performance and service life, both before and after seismic activity. The data generated from the embedded and surface mounted sensors will be analysed to allow an innovative and real-time health monitoring solution where structural integrity is continuously known. This indication of building performance will allow the structure to alert owners, engineers and asset managers of developing problems prior to failure thresholds being reached. A range of potential sensor technologies for monitoring the performance of existing and newly constructed concrete buildings is discussed. A description of monitoring work conducted on existing buildings during the July 2013 Cook Strait earthquake sequence is included, along with details of current work that investigates the performance of sensing technologies for detecting crack formation in concrete specimens. The potential market for managing the real-time health of installed infrastructure is huge. Civil structures all over the world require regular visual inspections in order to determine their structural integrity. The information recorded during the Christchurch rebuild will generate crucial data sets that will be beneficial in understanding the behaviour of concrete over the complete life cycle of the structure, from construction through to operation and building repairs until the time of failure. VoR - Version of Record
Predicting building collapse due to seismic motion is critical in design and more so after a major event. Damaged structures can appear sound, but collapse under following major events. There can thus be significant risk in decision making after a major seismic event concerning the safe occupation of a building or surrounding areas, versus the unknown impact of unknown major aftershocks. Model-based pushover analyses are effective if the structural properties are well understood, which is not valid post-event when this risk information is most useful. This research combines Hysteresis Loop Analysis (HLA) structural health monitoring (SHM) and Incremental Dynamic Analysis (IDA) methods to determine collapse capacity and probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. The nonlinear dynamic analysis method presented enables constant updating of building performance predictions using post-event SHM results. The resulting combined methods provide near real-time updating of collapse fragility curves as events progress, quantifying the change of collapse probability or seismic induced losses for decision-making - a novel, higher resolution risk analysis than previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model. Results show significant potential benefits and a clear evolution of risk. They also show clear need for extending SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010-2011 had significant post-event aftershocks after each main event. Finally, the overall method is generalisable to any typical engineering demand parameter.
Quick and reliable assessment of the condition of bridges in a transportation network after an earthquake can greatly assist immediate post-disaster response and long-term recovery. However, experience shows that available resources, such as qualified inspectors and engineers, will typically be stretched for such tasks. Structural health monitoring (SHM) systems can therefore make a real difference in this context. SHM, however, needs to be deployed in a strategic manner and integrated into the overall disaster response plans and actions to maximize its benefits. This study presents, in its first part, a framework of how this can be achieved. Since it will not be feasible, or indeed necessary, to use SHM on every bridge, it is necessary to prioritize bridges within individual networks for SHM deployment. A methodology for such prioritization based on structural and geotechnical seismic risks affecting bridges and their importance within a network is proposed in the second part. An example using the methodology application to selected bridges in the medium-sized transportation network of Wellington, New Zealand is provided. The third part of the paper is concerned with using monitoring data for quick assessment of bridge condition and damage after an earthquake. Depending on the bridge risk profile, it is envisaged that data will be obtained from either local or national seismic monitoring arrays or SHM systems installed on bridges. A method using artificial neural networks is proposed for using data from a seismic array to infer key ground motion parameters at an arbitrary bridges site. The methodology is applied to seismic data collected in Christchurch, New Zealand. Finally, how such ground motion parameters can be used in bridge damage and condition assessment is outlined. AM - Accepted manuscript
This work investigates the possibility of developing a non-contact, non-line of sight sensor to measure interstorey drift through simulation and experimental validation. • The method uses frequency-modulated continuous wave (FMCW) radar to measure displacement. This method is commonly in use in a number of modern applications, including aircraft altimeters and automotive parking sensors. • The technique avoids numerous problems found in contemporary structural health monitoring methods, namely integral drift errors and structural modification requirements. • The smallest achievable detection error in displacement was found to be as low as 0.26%, through simulated against the displacement response of a single degree of freedom structure subject to ground motion excitation. • This was verified during experimentation, when a corner-style reflector was placed on a shake table running ground motion data taken from the 4th September 2010 earthquake in Christchurch. These results confirmed the conclusions drawn from simulation.
Rapid, reliable information on earthquake-affected structures' current damage/health conditions and predicting what would happen to these structures under future seismic events play a vital role in accelerating post-event evaluations, leading to optimized on-time decisions. Such rapid and informative post-event evaluations are crucial for earthquake-prone areas, where each earthquake can potentially trigger a series of significant aftershocks, endangering the community's health and wealth by further damaging the already-affected structures. Such reliable post-earthquake evaluations can provide information to decide whether an affected structure is safe to stay in operation, thus saving many lives. Furthermore, they can lead to more optimal recovery plans, thus saving costs and time. The inherent deficiency of visual-based post-earthquake evaluations and the importance of structural health monitoring (SHM) methods and SHM instrumentation have been highlighted within this thesis, using two earthquake-affected structures in New Zealand: 1) the Canterbury Television (CTV) building, Christchurch; 2) the Bank of New Zealand (BNZ) building, Wellington. For the first time, this thesis verifies the theoretically- and experimentally validated hysteresis loop analysis (HLA) SHM method for the real-world instrumented structure of the BNZ building, which was damaged severely due to three earthquakes. Results indicate the HLA-SHM method can accurately estimate elastic stiffness degradation for this reinforced concrete (RC) pinched structure across the three earthquakes, which remained unseen until after the third seismic event. Furthermore, the HLA results help investigate the pinching effects on the BNZ building's seismic response. This thesis introduces a novel digital clone modelling method based on the robust and accurate SHM results delivered by the HLA method for physical parameters of the monitored structure and basis functions predicting the changes of these physical parameters due to future earthquake excitations. Contrary to artificial intelligence (AI) based predictive methods with black-box designs, the proposed predictive method is entirely mechanics-based with an explicitly-understandable design, making them more trusted and explicable to stakeholders engaging in post-earthquake evaluations, such as building owners and insurance firms. The proposed digital clone modelling framework is validated using the BNZ building and an experimental RC test structure damaged severely due to three successive shake-table excitations. In both structures, structural damage intensifies the pinching effects in hysteresis responses. Results show the basis functions identified from the HLA-SHM results for both structures under Event 1 can online estimate structural damage due to subsequent Events 2-3 from the measured structural responses, making them valuable tool for rapid warning systems. Moreover, the digital twins derived for these two structures under Event 1 can successfully predict structural responses and damage under Events 2-3, which can be integrated with the incremental dynamic analysis (IDA) method to assess structural collapse and its financial risks. Furthermore, it enables multi-step IDA to evaluate earthquake series' impacts on structures. Overall, this thesis develops an efficient method for providing reliable information on earthquake-affected structures' current and future status during or immediately after an earthquake, considerably guaranteeing safety. Significant validation is implemented against both experimental and real data of RC structures, which thus clearly indicate the accurate predictive performance of this HLA-based method.
Artificial Neural Networks (ANN) as a tool offers opportunities for modeling the inherent complexity and uncertainty associated with socio-environmental systems. This study draws on New Zealand ski
fields (multiple locations) as socio- environmental systems while considering their perceived resilience to low
probability but potential high consequences catastrophic natural events (specifically earthquakes). We gathered
data at several ski fields using a mixed methodology including: geomorphic assessment, qualitative interviews,
and an adaptation of Ozesmi and Ozesmi’s (2003) multi-step fuzzy cognitive mapping (FCM) approach. The data
gathered from FCM are qualitatively condensed, and aggregated to three different participant social groups. The
social groups include ski fields users, ski industry workers, and ski field managers. Both quantitative and
qualitative indices are used to analyze social cognitive maps to identify critical nodes for ANN simulations. The
simulations experiment with auto-associative neural networks for developing adaptive preparation, response and
recovery strategies. Moreover, simulations attempt to identify key priorities for preparation, response, and
recovery for improving resilience to earthquakes in these complex and dynamic environments. The novel mixed
methodology is presented as a means of linking physical and social sciences in high complexity, high uncertainty
socio-environmental systems. Simulation results indicate that participants perceived that increases in Social
Preparation Action, Social Preparation Resources, Social Response Action and Social Response Resources have
a positive benefit in improving the resilience to earthquakes of ski fields’ stakeholders.
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
