Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing

This paper employs a simplified analytical method to investigate the influence of reaction piles on the load–displacement behaviour of a test pile in static load testing. A parametric study is conducted to examine the effects of factors such as pile spacing ratio, pile slenderness ratio, and pile soil stiffness ratio. Several soil profiles are considered in the study. The parametric study also includes the cases of static load testing in the lateral direction. The correction factors for the initial pile head stiffness obtained from static pile load tests with the use of reaction piles are given in charts. Furthermore, analyses of centrifuge modelling of axially loaded piles are carried out using the simplified analytical method. Good agreements between the test results and the analysis results are demonstrated, and the applicability of the correction factors is verified.Key words: reaction piles, interaction, initial pile head stiffness, static vertical load testing, static lateral load testing, simplified analytical method.

Estimation of bearing capacity and settlement plays an essential role in designing pile foundations of buildings. This paper presents the results of pile static and dynamic loading tests taken place in the construction sites of Caspian Sea Port Prorva and Tengiz. The static loading tests (SLT) and static compression loading tests (SCLT) were carried out according to the Kazakhstan GOST and ASTM standards correspondingly. The dynamic loading tests (DLT) were performed by GOST, as well as by pile dynamic analyzer (PDA) in accordance with ASTM standards. According to the results of DLT of driven joint piles (with a cross-section of 40×40 cm, lengths of 25 m and 27 m) performed by PDA, the bearing capacity of the piles was 2143 kN. In the construction site of Prorva, the bearing capacities of driven joint piles according to the results of SCLT were2067 kN and 2042 kN. In the construction site of Tengiz, the bearing capacities of driven piles tested by SLT were 1000 kN and 1605kN. These investigations are important for the understanding of soil-structure interaction, especially of driven piles with a cross-section of 40x40 cm and lengths of 12 and 16m in construction site Tengiz.

This article addresses the problem of moving source localization in 3-D using the time-difference-of-arrival and frequency-difference-of-arrival measurements without accurate sensor location information. Different from the previous studies, we focus on improving the localization performance by introducing two approaches with a limited number of sensors, including optimal sensor placement and velocity configuration, as well as successive measurements for a time period. This article first determines the sensor placement and velocity (including speed and orientation) configuration to achieve optimal localization based on a criterion devised from the Fisher information matrix, in which the restrictions for the two-stage weighted-least-squares (TSWLS) positioning algorithm and the geographical conditions are taken into considerations. Then, to utilize successive measurements while the source is in motion, we separate the sensor velocity into two components for achieving the localization optimality and compensating the source motion, and propose a practical sensor placement and velocity configuration strategy. Finally, combining with the TSWLS-based prelocalization, Kalman filtering and the proposed sensor motion strategy, we develop a source tracking scheme that simultaneously obeys the optimal sensor placement and velocity configuration during tracking. Mathematical analyses and numerical simulations validate the optimal sensor placement and the efficiencies of the source tracking scheme.

This paper examines shaft and base grouted concrete piles by conducting vertical static load tests (SLTs) and dynamic load tests. Three concrete piles with shaft and base grouting, with base grouting only, and without grouting techniques were selected, and compressive SLTs were conducted. Two piles with grouting were also assessed with dynamic load tests. Another two uplift SLTs were conducted to one shaft and base grouted pile and one pile without grouting. Traditional presentations were provided to check whether the bored piles reached the design requirement. Interpretations of test results were also provided to determine the ultimate pile capacity. Results from these 5 SLT programs indicated that double-tangent and DeBeer's methods are close to each other, and Chin's method overestimates the pile capacity. Comparison of the results from the SLTs and dynamic load tests shows that the results from Chin's method are close to dynamic results, and Mazurkiewicz's method overestimates for friction resistance. The results also demonstrate that base and shaft grouted pile and base grouted pile increase by 9.82% and 2.89% in compressive capacity, respectively, and compared to the uplift SLTs; there is a 15.7% increment in pile capacity after using base and shaft grouting technology.

Pilesr are both statically and dynami cally tried to acquire the limit and to check outline. The two sorts will give comes about that may differ in light of the technique utilized as a part of leading the test. It is thusly, important to think about the aftereffects of a static load test with dynamic load test. Numerous comparison studies are directed around the world, yet the vast majority of them are for displacement driven pile. In this way, the consequences of the test are looked at for substitution-exhausted piles. The piles are tested statically before dynamic test. The test results show that a not too bad comprehension was expert between both the tests with in addition to less 7mm at design load regarding settlement. Moderately, the settlement foreseen in powerful load test is littler diverged from static load test. Regarding absolute bearing limit, Davisson's technique gives the insignificant outrageous load regard diverged from various strategies. The Davisson's strategy is utilized to analyze the outcomes since it is more moderate. The examination shows that the piles are inside 15% in regard as far as possible traversed Davisson's strategy. Since the static test was coordinated before unique test, the limit got from dynamic test is higher because of the pile experienced flexible pressure amid static load test and furthermore because of soil setup.

An instrumented test pile was installed at the Bayou Zourie bridge reconstruction site as part of a Louisiana Department of Transportation and Development (LADOTD) research initiative to study the setup phenomenon of piles driven in Louisiana soils. Pile instrumentation included pressure cells to measure the total pressure at the pile face, piezometers to monitor the excess pore water pressure at the pile face, and “sister bar” strain gauges to measure the strain distribution along the pile. Additional instrumentation consisted of multilevel piezometers installed within soils at different locations/depths from the pile and accelerometers attached to the piles during dynamic load testing. A total of two static load tests and four dynamic load tests were conducted on the test pile. During the static load tests, the strains within the pile were measured by the strain gauges, which were used to calculate the distribution of load transfer along the pile. Both static and dynamic load tests demonstrated the increase in pile resistance with time (setup). Results of dynamic load tests confirmed that pile setup occurs at a logarithmic rate after the end of driving (EOD) and is mainly attributed to the increase in side resistance. Good correlation was observed in this study between the pile setup and the percentage of dissipated excess pore water pressure with time. The measured excess pore water pressure suggested that the surrounding soil, along the pile (within distance 2B), is significantly influenced during pile driving. Results indicated that the changes in side resistance are directly related to the changes in the horizontal effective stress acting on the pile face.

The Port Mann Bridge on Highway 1 in Metro Vancouver links the cities of Surrey and Coquitlam in British Columbia, Canada. A new 10-lane 2.02 km long cable-stayed bridge is being constructed over the Fraser River to replace the existing 5-lane bridge. Twelve of the twenty-five bridge piers will be supported on driven steel pipe piles. As part of the foundation design approach, a full-scale instrumented static pile load test was performed along with dynamic pile testing. The test and reaction piles consist of 75 m long, 1,830 mm diameter by 25 mm thick, open-ended steel pipe piles driven into glacially overridden soil using an APE D-180-42 diesel hammer. A custom-built static load frame and hydraulic jacking system provided incremental compressive loads up to 53 MN on the test pile. Following the compression test, an uplift test to 29 MN was performed. The pile was restruck with a Menck MHU 500T hydraulic hammer 9 days after the uplift test. Evaluation of the pile resistance distribution included impacts of residual load and pile plugging. Pile performance during the static load test indicated that approximately 36 MN was carried in side resistance and approximately 17 MN was provided in base resistance. The static pile load test and dynamic testing of selected production piles enabled the use of increased resistance factors for final design, thereby reducing the number of piles to 255, a reduction of approximately 30 percent.

No AccessTechnical NoteClustering of Sensor Locations Using the Effective Independence MethodMichael I. Friswell and Rafael Castro-TrigueroMichael I. FriswellCollege of Engineering, Swansea University, Swansea SA2 8PP, United Kingdom*Professor of Aerospace Structures, College of Engineering; .Search for more papers by this author and Rafael Castro-TrigueroDepartment of Mechanics, University of Cordoba, 14071 Cordoba, Spain†Department of Mechanics, Campus de Rabanales.Search for more papers by this authorPublished Online:12 Mar 2015https://doi.org/10.2514/1.J053503SectionsView Full TextPDFPDF Plus ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Pickrel C. R., “A Practical Approach to Modal Pretest Design,” Mechanical Systems and Signal Processing, Vol. 13, No. 2, 1999, pp. 271–295. doi:https://doi.org/10.1006/mssp.1998.1212 0888-3270 CrossrefGoogle Scholar[2] Penny J. E. T., Friswell M. I. and Garvey S. D., “Automatic Choice of Measurement Locations for Dynamic Testing,” AIAA Journal, Vol. 32, No. 2, 1994, pp. 407–414. doi:https://doi.org/10.2514/3.11998 AIAJAH 0001-1452 LinkGoogle Scholar[3] Papadopoulos M. and Garcia E., “Sensor Placement Methodologies for Dynamic Testing,” AIAA Journal, Vol. 36, No. 2, 1998, pp. 256–263. doi:https://doi.org/10.2514/2.7509 AIAJAH 0001-1452 LinkGoogle Scholar[4] Garvey S. D., Friswell M. I. and Penny J. E. T., “Evaluation of a Method for Automatic Selection of Measurement Locations Based on Subspace-Matching,” XIV International Modal Analysis Conference (IMAC), Society of Experimental Mechanics, Bethel, CT, Feb. 1996, pp. 1546–1552. Google Scholar[5] He L., Lian J., Ma B. and Wang H., “Optimal Multiaxial Sensor Placement for Modal Identification of Large Structures,” Structural Control and Health Monitoring, Vol. 21, No. 1, 2014, pp. 61–79. doi:https://doi.org/10.1002/stc.1550 CrossrefGoogle Scholar[6] D'Souza K. and Epureanu B., “Sensor Placement for Damage Detection in Nonlinear Systems Using System Augmentations,” 48th AIAA/American Soc. of Mechanical Engineers/American Helicopter Soc./American Society for Composites Structures, Structural Dynamics, and Materials Conference, AIAA Paper 2007-2057, 2007. LinkGoogle Scholar[7] Reynier M. and Kandil H. A., “Sensors Location for Updating Problems,” Mechanical Systems and Signal Processing, Vol. 13, No. 2, 1999, pp. 297–314. doi:https://doi.org/10.1006/mssp.1998.1213 0888-3270 CrossrefGoogle Scholar[8] Castro-Triguero R., Murugan S. M., Gallego R. and Friswell M. I., “Robust Sensor Placement under Parametric Uncertainty,” Mechanical Systems and Signal Processing, Vol. 41, Nos. 1–2, Dec. 2013, pp. 268–287. doi:https://doi.org/10.1016/j.ymssp.2013.06.022 0888-3270 CrossrefGoogle Scholar[9] Castro-Triguero R., Saavedra Flores E. I., Diaz de la O F. A., Friswell M. I. and Gallego R., “Optimal Sensor Placement in Timber Structures by Means of a Multi-Scale Approach with Material Uncertainty,” Structural Control and Health Monitoring, Vol. 21, No. 12, Dec. 2014, pp. 1437–1452. doi:https://doi.org/10.1002/stc.1654 CrossrefGoogle Scholar[10] Kammer D. C., “Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures,” Journal of Guidance, Control and Dynamics, Vol. 14, No. 2, 1991, pp. 251–259. doi:https://doi.org/10.2514/3.20635 LinkGoogle Scholar[11] Kammer D. C. and Tinker M. L., “Optimal Placement of Triaxial Accelerometers for Modal Vibration Tests,” Mechanical Systems and Signal Processing, Vol. 18, No. 1, 2004, pp. 29–41. doi:https://doi.org/10.1016/S0888-3270(03)00017-7 0888-3270 CrossrefGoogle Scholar[12] Kammer D. C. and Peck J. A., “Mass-Weighting Methods for Sensor Placement Using Sensor Set Expansion Techniques,” Mechanical Systems and Signal Processing, Vol. 22, No. 7, 2008, pp. 1515–1525. doi:https://doi.org/10.1016/j.ymssp.2008.01.002 0888-3270 CrossrefGoogle Scholar[13] Hemez F. M. and Farhat C., “An Energy Based Optimum Sensor Placement Criterion and Its Application to Structural Damage Detection,” XII International Modal Analysis Conference (IMAC), Society of Experimental Mechanics, Bethel, CT, 1994, pp. 1568–1575. Google Scholar[14] Heo G., Wang M. L. and Satpathi D., “Optimal Transducer Placement for Health Monitoring of Long Span Bridge,” Soil Dynamics and Earthquake Engineering, Vol. 16, Nos. 7–8, 1997, pp. 495–502. doi:https://doi.org/10.1016/S0267-7261(97)00010-9 CrossrefGoogle Scholar[15] Papadimitriou C. and Beck J. L., “Entropy-Based Optimal Sensor Location for Structural Model Updating,” Journal of Vibration and Control, Vol. 6, No. 5, 2000, pp. 781–800. doi:https://doi.org/10.1177/107754630000600508 CrossrefGoogle Scholar[16] Papadimitriou C., “Optimal Sensor Placement Methodology for Parametric Identification of Structural Systems,” Journal of Sound and Vibration, Vol. 278, Nos. 4–5, 2004, pp. 923–947. doi:https://doi.org/10.1016/j.jsv.2003.10.063 JSVIAG 0022-460X CrossrefGoogle Scholar[17] Li D. S., Li H. N. and Fritzen C. P., “Load Dependent Sensor Placement Method: Theory and Experimental Validation,” Mechanical Systems and Signal Processing, Vol. 31, Aug. 2012, pp. 217–227. doi:https://doi.org/10.1016/j.ymssp.2012.04.014 0888-3270 CrossrefGoogle Scholar[18] Rao A. R. M. and Anandakumar G., “Optimal Sensor Placement Techniques for System Identification and Health Monitoring of Civil Structures,” Smart Structures and Systems, Vol. 4, No. 4, 2008, pp. 465–492. doi:https://doi.org/10.12989/sss.2008.4.4.465 CrossrefGoogle Scholar[19] Chang M. and Pakzad S. N., “Optimal Sensor Placement for Modal Identification of Bridge Systems Considering Number of Sensing Nodes,” Journal of Bridge Engineering, Vol. 19, No. 6, 2014, Paper 04014019. doi:https://doi.org/10.1061/(ASCE)BE.1943-5592.0000594 CrossrefGoogle Scholar Previous article Next article

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.

Tensegrity structures are systems composed of elements in compression and tension in a stable self-equilibrium state that provides stability and stiffness to the structure. Tensegrity finds its root in contemporary art with Kenneth Snelson’s sculptures, yet it quickly evolved into a structural paradigm employed in a wide spectrum of science and engineering applications. Tensegrity structures being lightweight, and capable of combining sensors and actuators with structural elements, they are advantageous for active applications. However, in most active applications, sensor placement is based on engineering judgement and not a systematic approach based on the analysis of tensegrity structures. This paper addresses sensor placement for structural identification and damage detection in tensegrity structures using cellular decomposition. By decomposing tensegrity structures into the minimum number of constitutive unicellular sub-structures (cells and stable sub-structures resulting from their interaction), the minimum number of sensors required for their self-stress identification can be defined along with a set of edge solutions for sensor placement. Moreover, under the assumptions of a known deformed geometry and loading, it is shown that the resulting sensor configurations can be extended for structural identification as well as damage detection providing a theoretical framework for active and sensory tensegrity structures based on their cellular composition.

Structural health monitoring (SHM) can extend the operation of bridges beyond their original life span, increase the safety between scheduled inspections, and allow for a prioritized inspection after extreme events. One of the major challenges is to assess which damages can or cannot be diagnosed (i.e., detected or localized), which is essential to evaluate the value of a SHM system before it is installed, and to optimize the sensor placement accordingly. This work develops a framework to predict the minimum damage, i.e., the minimum change in local structural design parameters that can reliably be detected based on changes in global damage-sensitive features. The diagnosis is considered reliable if the probability of false alarms is low and the probability of detection is high. Equivalently, a damage is detectable if it is significant under consideration of typical uncertainties related to ambient excitation and measurement noise and empirical safety thresholds. The approach requires vibration data from the undamaged structure in combination with a numerical model, and is universally applicable to a wide range of structures and damage-sensitive features. Secondly, a method is proposed to analyze the minimum localizable damage. The results show that optimal localizability is a compromise between high localization resolution, high detectability, and few false localization alarms. Thirdly, a sensor placement strategy is devised that takes as input the desired minimum diagnosable damage and optimizes the sensor layout and the number of sensors accordingly. The method allows one to focus the global damage diagnosis on local structural components. Ultimately, the monitoring of prestressing forces and support displacements is incorporated into the diagnostic framework, so that they can be analyzed and distinguished from changes in material properties or cross-sectional values. Besides the performance evaluation, the framework is suitable for quality control of existing instrumentation on real structures. Therefore, self-validation strategies are implemented to verify the input parameters, to validate the theoretical assumptions, and to check its effectiveness based on non-invasive tests using extra masses. The proof of concept studies based on a laboratory steel beam and a cable-stayed bridge show promising results regarding the practical application of the theoretical contributions.

Performance-based optimal sensor placement method for single-layer reticulated shells considering modal observability and damage identifiability

As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple types of environmental characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluated the proposed approach via an application in air quality monitoring scenario in Hong Kong and experimental results demonstrate the effectiveness of the proposed approach.

This paper deals with the prediction of shaft resistance of single driven pile based on Meyerhoff formulation and empirical method based on SPT test. The accuracy of these predictions was evaluated by comparing the results with static load test (SLT) and dynamic loading test analyzed by Pile Driving Analyzer (PDA) and Case Pile Wave Analysis Program (CAPWAP) programs. The data for this study was collected from University Riau Hospital Project in Pekanbaru. The result of static and dynamic pile load test shows that only a small capacity of end resistance was mobilized due to insufficient pile deformation.

Wearable sensors for personal temperature exposure assessments: A comparative study