Uncertainty and sensitivity analysis in soil strata model generation for ground settlement risk evaluation

Popular summary: Ground settlement due to groundwater drainage during construction is important to be considered since ground settlement may cause severe building damages. The calculation of ground settlement contains several parameters with uncertainties, e.g., soil layer thickness and elevations, compression and consolidation properties of different soils. If the uncertainties in the parameters are defined, the uncertainties in ground settlement could be estimated. By addressing the most influential parameters on the ground settlement in a certain area, the uncertainties in the ground settlement could be decreased. In this thesis, a three dimensional soil layer model was generated for ground settlement risk evaluation purpose. The accurate prediction of soil layers around the construction site could help better estimation of ground settlement. The uncertainties in the soil layer model were quantified and then the soil layer model was integrated with two other models (groundwater and ground settlement model) for the ground settlement risk evaluation. The case study site was located in Motala, Sweden where a pedestrian tunnel was considered to be built. The soil layer model was interpolated from borehole data - holes drilled into the ground for soil layer investigation. The interpolation technique used here was called kriging. The uncertainties in the soil layer model came from the interpolation process and were quantified. The risk evaluation of the ground settlement was carried out by Monte Carlo simulation which is a kind of stochastic modeling method. The resulted soil layer model could show soil layers formation at any specified site. The quality of interpolation was largely dependent on the amount of boreholes. The soil layer model needed to be adjusted manually since sometimes the interpolated soil layers overlapped each other. The ground settlement risk evaluation results illustrated the risk areas for building damage where the calculated ground settlement exceeded critical values. The most influential parameters on ground settlement were found varied in different places. More efforts and resources could be spent on these parameters to decrease the unacceptable risks. The kriging method used here was a sophisticated method and there is room for improvements.Scientific summary: Ground settlement due to groundwater drainage during construction is important to be considered since ground settlement may cause severe building damages. The calculation of ground settlement contained several parameters with different magnitude of uncertainties. Thus a risk evaluation of ground settlement is necessary. The aim of this thesis was first to build a soil strata model for ground settlement risk evaluation purpose. Second was to carry out the uncertainty and sensitivity analysis of the soil strata model. Third was to carry out the ground settlement risk evaluation by integrating soil strata model and two other models, with defined uncertainties of each model. The case study site was located in Motala, Sweden with area about 0.39 km2. The soil strata model was generated by utilizing kriging interpolation. The continuous elevations of each soil layer in the soil strata were interpolated from boreholes and then all the soil layers were combined to create a “layer-cake model”. The uncertainty in kriging was quantified by prediction standard error. By utilizing Monte Carlo simulation, the stochastic representation of the soil strata was created and the uncertainty and sensitivity analysis of the soil strata model was carried out. The risk evaluation of ground settlement was conducted by carrying out Monte Carlo simulation for the integrated model of soil strata, groundwater and ground settlement. The uncertainties of the soil strata model were mapped in the form of median, standard deviation, skewnesss, etc. from different soil layers. From sensitivity analysis, it could be inferred that the most influential parameters on the thickness a soil layer would be the upper and lower boundary elevations of that layer. The risk areas of building damage have been mapped where the 50th and 95th percentile of the calculated ground settlement exceeded critical values. The most influential parameters on ground settlement were found varied in different places. More efforts and resources could be spent on these parameters to decrease the unacceptable risks. It was conclude that kriging interpolation was an effective way for generating soil strata model from boreholes. Keywords: Kriging, Monte Carlo simulation, Soil strata, Uncertainty analysis, Sensitivity analysis, Risk analysi

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

PROPOSAL OF A SEQUENTIAL METHOD FOR SPATIAL INTERPOLATION OF MODE CHOICE

The main objective of this study is to propose a sequential method for spatial interpolation of mode choice for household locations where choices are unobserved based on Decision Tree analysis and Geostatistics. Initially, Decision Tree analysis was applied in order to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be estimated by Ordinary Kriging. The data used is from the Origin-Destination Survey and Urban Transportation Evaluation Survey, carried out in 2007/2008 in the city of São Carlos (São Paulo/Brazil). The study area selected for geoestatistical modeling is a small region of the city with 110 sampling points. The mode choice was estimated for the study area revealing a tendency of increasing the probability of car usage from the center to the periphery of region. The proposed method can be an alternative to traditional approaches in both non-spatial modeling, especially for the case of lack of data from stated preference survey, as in spatial modeling, allowing estimation in various geographic coordinates

Geostatistical Interpolation and Analyses of Washington State AADT Data from 2009 ? 2016

Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result

Acoustic mapping based on measurements : space and time interpolation

International audienc

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

Hybrid structural health monitoring using data-driven modal analysis and model-based Bayesian inference.

Civil infrastructures that are valuable assets for the public and owners must be adequately and periodically maintained to guarantee safety, continuous service, and avoid economic losses. Vibration-based structural health monitoring (VBSHM) has been a significant tool to assess the structural performance of civil infrastructures over the last decades. Challenges in VBSHM exist in two aspects: operational modal analysis (OMA) and Finite element model updating (FEMU). The former aims to extract natural frequency, damping ratio, and mode shapes using vibrational data under normal operation; the latter focuses on minimizing the discrepancies between measurements and model prediction. The main impediments to real-world application of VBSHM include 1) uncertainties are inevitably involved due to measurement noise and modeling error; 2) computational burden in analyzing massive data and high-fidelity model; 3) updating structural coupled parameters, e.g., mass and stiffness. Bayesian model updating approach (BMUA) is an advanced FEMU technique to update structural parameters using modal data and account for underlying uncertainties. However, traditional BMUA generally assumes mass is precisely known and only updating stiffness to circumvent the coupling effect of mass and stiffness. Simultaneously updating mass and stiffness is necessary to fully understand the structural integrity, especially when the mass has a relatively large variation. To tackle these challenges, this dissertation proposed a hybrid framework using data-driven and model-based approaches in two sequential phases: automated OMA and a BMUA with added mass/stiffness. Automated stochastic subspace identification (SSI) and Bayesian modal identification are firstly developed to acquire modal properties. Following by a novel BMUA, new eigen-equations based on two sets of modal data from the original and modified system with added mass or stiffness are derived to address the coupling effect of structural parameters, e.g., mass and stiffness. To avoid multi-dimensional integrals, an asymptotic optimization method and Differential Evolutionary Adaptive Metropolis (DREAM) sampling algorithm are employed for Bayesian inference. To alleviate computational burden, variance-based global sensitivity analysis to reduce model dimensionality and Kriging model to substitute time-consuming FEM are integrated into BMUA. The proposed VBSHM are verified and illustrated using numerical, laboratory and field test data, achieving following goals: 1) properly treating parameter uncertainties; 2) substantially reducing the computational cost; 3) simultaneously updating structural parameters with addressing the coupling effect; 4) performing the probabilistic damage identification at an accurate level

DIAGNOSTIC ET ANALYSE DE L’ENVIRONNEMENT ACOUSTIQUE DES CONFIGURATIONS URBAINES SAHARIENNES. CAS DE BISKRA.

La pollution sonore affecte une part considérable de la population mondiale, y compris les habitants Sahariens. L’expansion proliférante des villes et le trafic routier constituent les principales causes de la recrudescence des effets négatifs du bruit sur l’environnement et la qualité de vie de ces habitants. Par ailleurs, les pays développés et les agglomérations Sahariennes connaissent un manque de données acoustiques et une énorme lacune en matière des normes et des procédures nécessaires à la planification urbaine. La présente étude vise à examiner l’environnement acoustique des différents tissus urbains constituant la ville de Biskra, comme elle s’appuie principalement sur des approches méthodologiques multidisciplinaires traitant la dimension objective, voire subjective. Premièrement, la théorie de la Syntaxe Spatiale visant à analyser la morphologie urbaine. En se focalisant sur l’analyse des segments angulaires qui permet un diagnostic détaillé des propriétés de l’avant-plan et de l’arrière-plan. Le potentiel des deux concepts de « à-mouvement » et « à travers-mouvement » permet une exploration perspicace des mouvements mécaniques et piétonniers à l'échelle locale et globale. Par conséquent, plusieurs rayons métriques ont été utilisés : 400 m, 800 m, 1200 m, 1600 m, 2000 m, 2400 m et 3200 m. Ensuite, une approche expérimentale, consistant à évaluer l’environnement acoustique en effectuant 240 stations de mesures à l’aide d’un sonomètre étalonné. Ces stations, enregistrant chacune 600 valeurs de niveau sonore équivalent continu pondéré A (LeqA), sont pour la plupart installées à proximité des zones résidentielles et des bords des principales autoroutes, voies de circulation et axes piétonniers. Les données obtenues ont été modélisées à l’aide de différents modèles d’interpolation fournis par le système d’information géographique (QGIS et SAGA GIS), notamment : Distance Inverse Pondérée (Gaussienne, Exponentielle, Quadratique K2) et Krigeage (Ordinaire, Universel). Finalement, l’approche subjective consiste à élaborer un sondage abordant trois axes distincts : la qualité affective perçue, la carte mentale sonore et les préférences en matière de paysage sonore. Les résultats mettent en évidence le caractère bruyant de Biskra, tout en soulignant l’efficacité du model IDW. Ils démontrent aussi une corrélation positive moyenne à forte des mesures syntaxiques à l’échelle globale et locale, impliquant une explication partielle des configurations urbaines et acoustiques par ces variables spatiales. Cette étude approfondie constitue un point de départ pour soulever cette question auprès des planificateurs et décideurs de la ville afin de créer un plan d’action pratique pour une stratégie de développement durable

Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation

This study aims to investigate the exposure to air pollution in Seoul and the consequent health effects in Seoul South Korea, and suggest possible solutions using agent-based modelling (ABM). ABM is a useful technique that can simulate pollution generation and exposure, mobility patterns of unique individuals, and explore future scenarios. The first study compared Universal Kriging and Generalised Additive Models to spatially interpolate pollution station data over Seoul. A new method was discovered to enhance the accuracy of NO2 on roads. Next, ABM was used to evaluate potential health loss for a set of demographic groups after being cumulatively exposed to particulates (PM10), with a nominal heath impact threshold of 100µg/m3. Finally, a traffic simulation examined the coupled problem of non-exhaust emissions and behaviour and estimate exposure to PM10 for groups of drivers and pedestrians in central Seoul. Having tested the sensitivity to calibrated parameters, scenarios of traffic restriction and modification of pedestrian behaviour to avoid polluted areas was investigated. With less difference between interpolation methods, the result showed a remarkable contrast between roadside and background NO2 as well as a daily cycle, while PM10 had a small variance between hours but had greater seasonal oscillation. The first ABM study showed that disparities in health may arise as a result of differences in socioeconomic status, especially when the group was exposed over a long period, and road proximity caused additional health loss. In the traffic simulation study, extreme PM10 was found along roadways, but although drivers were exposed to extreme values, longer exposure for pedestrians led to higher health risks. Despite the absence of reliable data linking exposure to actual health effects, it is possible to make progress with ABM. In addition, pollution exposure can vary by commuting patterns and the urban development of one’s location. Scenarios can be advantageous for healthcare policy – to aid the most vulnerable groups and districts

Road distance and travel time for spatial urban modelling

Interactions within and between urban environments include the price of houses, the flow of traffic and the intensity of noise pollution, which can all be restricted by various physical, regulatory and customary barriers. Examples of such restrictions include buildings, one-way systems and pedestrian crossings. These constrictive features create challenges for predictive modelling in urban space, which are not fully captured when proximity-based models rely on the typically used Euclidean (straight line) distance metric. Over the course of this thesis, I ask three key questions in an attempt to identify how to improve spatial models in restricted urban areas. These are: (1) which distance function best models real world spatial interactions in an urban setting? (2) when, if ever, are non-Euclidean distance functions valid for urban spatial models? and (3) what is the best way to estimate the generalisation performance of urban models utilising spatial data? This thesis answers each of these questions through three contributions supporting the interdisciplinary domain of Urban Sciences. These contributions are: (1) the provision of an improved approximation of road distance and travel time networks to model urban spatial interactions; (2) the approximation of valid distance metrics from non-Euclidean inputs for improved spatial predictions and (3) the presentation of a road distance and travel time cross-validation metric to improve the estimation of urban model generalisation. Each of these contributions provide improvements against the current state-of-the-art. Throughout, all experiments utilise real world datasets in England and Wales, such datasets contain information on restricted roads, travel times, house sales and traffic counts. With these datasets, I display a number of case studies which show up to a 32% improved model accuracy against Euclidean distances and in some cases, a 90% improvement for the estimation of model generalisation performance. Combined, the contributions improve the way that proximity-based urban models perform and also provides a more accurate estimate of generalisation performance for predictive models in urban space. The main implication of these contributions to Urban Science is the ability to better model the challenges within a city based on how they interact with themselves and each other using an improved function of urban mobility, compared with the current state-of-the-art. Such challenges may include selecting the optimal locations for emergency services, identifying the causes of traffic incidents or estimating the density of air pollution. Additionally, the key implication of this research on geostatistics is that it provides the motivation and means of undertaking non-Euclidean based research for non-urban applications, for example predicting with alternative, non-road based, mobility patterns such as migrating animals, rivers and coast lines. Finally, the implication of my research to the real estate industry is significant, in which one can now improve the accuracy of the industry's state-of-the-art nationwide house price predictor, whilst also being able to more appropriately present their accuracy estimates for robustness