Mohamad Alipour

Leveraging Vision for Structural Identification: A Digital Image Correlation Based Approach

This paper describes a case study on a large scale structural test specimen using 3D-DIC as an image-based metrology approach for structural identification (St-ID). For the identification process, a commercial FEA software package was interfaced with MATLAB to converge on the optimal unknown/uncertain system parameters of the experimental setup. The 3D-DIC results provided a rich full-field dataset that was used in the identification process, which was compared against ground-truth measurements derived from traditional physical in-place sensors typically used in St-ID. For the identification, a novel hybrid algorithm, incorporating a combination of a genetic algorithm and a gradient-based scheme was utilized for updating the FEA model and obtaining the optimal values of the selected parameters. Results demonstrated that deflection, strain, and rotation measurements derived from DIC mirrored those from the ground-truth sensors and allowed for convergence of the updating with a variety of measurement responses that are challenging to acquire in typical applications.

Vibration Testing for Bridge Load Rating

Load rating is the process of determining the safe live-load carrying capacity of a bridge and is thus a major basis in prioritizing maintenance operations and allocation of resources. Traditionally, bridge evaluation standards provide two approaches to load rating, namely the analytical calculations and the empirical static load tests. Analytical load ratings are generally based on simplifying assumptions and may not closely reflect the current physical condition of the bridge. Empirical load tests provide a more realistic picture of live-load capacity of a bridge, but their application has been considerably limited by cost, time, test truck requirement, traffic interruption, and safety. Recently, a new approach named in this paper as “vibration-calibrated model-based load rating” has been investigated by a number of researchers. In this method, a vibration test is performed on the bridge and a refined finite element model is calibrated so as to replicate the observed modal response of the bridge. The calibrated model, which is adjusted to reflect the real in-situ condition and performance of the bridge, will then be used for the purpose of realistic load rating. This paper starts by reviewing the limitations of the current analytical and empirical methods and provides an in-depth explanation of the vibration-based method for load rating. Finally, recent research on this approach is reviewed and a discussion on the advantages, disadvantages and special considerations involved is presented. The areas that require more research and future work are also highlighted.

Pattern Recognition in the National Bridge Inventory for Automated Screening and the Assessment of Infrastructure

The size and complexity of the problem of maintaining the aging US transportation infrastructure system, combined with the shortage of resources, necessitates an efficient strategy to prioritize the allocation of funds. Within the suite of tools available for decision-making for bridges, a fundamental characteristic is safe load carrying capacity. This capacity measure typically requires knowledge and data on the structural details of the constituent members to enable predictions of available resistance relative to loading demands. Bridges that receive low ratings and are deemed incapable of carrying the required loads are “posted” with maximum weight limit signage. This paper introduces a data-driven solution that enables the automated, rapid, and costeffective evaluation of load postings for large infrastructure networks. The method proposed in this paper involves leveraging the large bridge population in the national bridge inventory and the associated bridge descriptors such as geometrical, operational, functional, and physical features, to extract and define patterns for predicting posting status. A cost-sensitive random forest classification algorithm was trained on over 280,000 bridges in selected categories in the national bridge inventory including steel, reinforced concrete, prestressed concrete, and timber bridges. Performance evaluation of the models demonstrated the validity of the models and comparisons with a number of other common classifiers was presented. The trained models were capable of detecting posted and unposted bridges with an average error of about 11% and 16% respectively. The trade-off between safety and economy in the models was also studied. Finally, as a product of the data-driven approach, an interactive software interface was developed which accepts user input data on bridges and predicts the posting status. This tool is expected to provide an intuitive method for rapid screening of bridge inventories and estimating deterioration progression, thereby resulting in substantial safety and financial benefits to owners.

Structural Stiffness Identification of Skewed Slab Bridges with Limited Information for Load Rating Purpose

This paper presents a method for identifying structural stiffness of skewed reinforced concrete slab bridges with limited structural information using measured acceleration data. This information might be used for nondestructive evaluation, condition assessment, and load rating of bridges. A large number of slab bridges with different structural dimensions such as skew angle, span, width, and thickness was first analyzed using finite element method to obtain their first modal frequency. This population of data was then used to create an artificial neural network, which can predict a coefficient that plays an important role in identifying the flexural rigidity of slab bridges. This approach was applied to estimate the flexural rigidity of a highly skewed reinforced concrete slab bridge in the state of Virginia for load rating purpose. The bridge was instrumented with wireless accelerometers, and the vibration responses of the bridge under ambient loading and impact hammer test were recorded. An algorithm based on the variational mode decomposition was employed to identify modal properties of the bridge. Then, the flexural rigidity of bridge was computed from the established relationship between the first natural frequency and the flexural rigidity of bridge. Results show that the proposed method is capable of predicting structural stiffness, and can be used for load rating of bridges without structural information.

Vibration Serviceability Assessment of an In-Service Pedestrian Bridge Under Human-Induced Excitations

Pedestrian bridges may experience significant vibrations under pedestrian traffic and wind loads. Design codes address the vibration limit state levels either by ensuring the frequency ranges associated with typical pedestrian passages are outside the lower fundamental frequencies of the structure or by restricting the maximum accelerations below the limits for pedestrian comfort. This paper discusses vibration serviceability assessment of a highly trafficked local pedestrian bridge based on the field dynamic tests. The selected bridge is a 60-m-long three-span steel structure with a continuous reinforced concrete slab supported on two longitudinal steel girders. First, a finite element model of the pedestrian bridge is developed to obtain the natural frequencies and mode shapes. Then, ambient vibration tests are conducted to validate the modal characteristics of the pedestrian bridge. Next, the dynamic response of the bridge in terms of peak accelerations is determined both experimentally and analytically under various pedestrian excitations. Finally, the implications of the results for the serviceability limit state assessment of the pedestrian bridge are discussed.

Load Rating of a Reinforced Concrete T-Beam Bridge Through Ambient Vibration Testing and Finite Element Model Updating

As the load demands on highway bridges increases, it is essential that the load rating procedures reliably assess the condition of existing structures. In addition, conventional design office load rating techniques cannot be used for bridges without structural plans, which indicates the need for a more advanced load rating procedure. This paper presents a methodology to compute the live load-carrying capacity of reinforced concrete T-beam bridges, which can be applied for bridges with structural plans or with missing or limited design information. The method involves modal identification of bridge using ambient vibrations and finite element model updating using vibration characteristics for capacity estimation. A simply supported T-beam bridge located in Virginia is selected for field-testing to verify the proposed method. The bridge is composed of five spans of the same length, 12.95 m for each, with a total length of 65.4 m and a width of 8.864 m. A total of nine accelerometers are installed to bridge to collect acceleration data for 15 min at a sampling rate of 500 Hz. The modal properties of the bridge are determined using enhanced frequency domain decomposition technique. The initial finite element model of the bridge is updated such that the modal properties of the bridge match the field measured parameters. The load effects and capacity of the bridge are determined and used to calculate the load rating factor. The rating factors obtained from the proposed method and traditional design office load rating procedures are compared. The results indicate that the proposed method can reveal the reserve capacity of bridges.

Reframing measurement for structural health monitoring: a full-field strategy for structural identification

Structural health monitoring (SHM) describes a decision-making framework that is fundamentally guided by state change detection of structural systems. This framework typically relies on the use of continuous or semi-continuous monitoring of measured response to quantify this state change in structural system behavior, which is often related to the initiation of some form of damage. Measurement approaches used for traditional SHM are numerous, but most are limited to either describing localized or global phenomena, making it challenging to characterize operational structural systems which exhibit both. In addition to these limitations in sensing, SHM has also suffered from the inherent robustness inherent to most full-scale structural systems, making it challenging to identify local damage. These challenges highlight the opportunity for alternative strategies for SHM, strategies that are able to provide data suitable to translate into rich information. This paper describes preliminary results from a refined structural identification (St-ID) approach using fullfield measurements derived from high-speed 3D Digital Image Correlation (HSDIC) to characterize uncertain parameters (i.e. boundary and constitutive properties) of a laboratory scale structural component. The St-ID approach builds from prior work by supplementing full-field deflection and strain response with vibration response derived from HSDIC. Inclusion of the modal characteristics within a hybrid-genetic algorithm optimization scheme allowed for simultaneous integration of mechanical and modal response, thus enabling a more robust St-ID strategy than could be achieved with traditional sensing techniques. The use of full-field data is shown to provide a more comprehensive representation of the global and local behavior, which in turn increases the robustness of the St-Id framework. This work serves as the foundation for a new paradigm in SHM that emphasizes characterizing structural performance using a smaller number, but richer set of measurements.

Numerical Investigation of the Shear Buckling and Post-Buckling of Thin Steel Plates with FRP Strengthening

The behavior of thin steel plates under shear loading is governed by early diagonal buckling and subsequent formation of an inclined tension field. This behavior describes the load carrying mechanism in deep steel plate girder webs and steel plate shear walls. Traditionally, steel stiffeners are used on the thin plate in some applications to mitigate out-of-plane buckling and encourage shear yielding, which is preferable and a more stable and energy-dissipating load carrying mechanism. However, the cost and practical difficulties associated with welding these stiffeners, especially on very thin plates and for in-service rehabilitation purposes, are considered as major drawbacks. In this paper, the behavior of thin steel plates strengthened with FRP strips was numerically investigated. FRP wraps were hypothesized to act as an elastic support for the thin steel plate in the early loading stages and as an auxiliary load transfer path in the inelastic tension field action. Numerical investigations were carried out using the finite element method and were divided into two phases; buckling and postbuckling. Elastic eigenvalue analysis for buckling and full inelastic analysis with geometrical and material nonlinearity for the post-buckling phase were carried out. FRP fracture and damage were incorporated in the models. Optimum angle of FRP fibers was studied both for buckling mitigation and post-buckling behavior enhancement. A number of FRP strengthening configurations together with a range of common FRP materials were also employed in the analyses. It was concluded that bonding FRP patches on the steel plate can effectively encourage behavior enhancements, especially given appropriate configuration and material properties.

The Citizen Engineer: Urban Infrastructure Monitoring via Crowd-Sourced Data Analytics

In the US, the challenges of an aging infrastructure network, coupled with requirements for maintaining continuous functionality of this network, highlights the need for innovative, and multi-disciplinary solutions aimed at the timely detection and remediation of defects and deterioration before serious failure situations materialize. Considering the constant and widespread interactions of citizens with urban infrastructure systems, and the increasing ubiquity of mobile and personal electronic devices equipped with onboard sensing capabilities (e.g. camera, accelerometers, GPS, etc.), the concept of leveraging crowd-sourcing provides a promising data-driven solution for urban infrastructure monitoring. In this approach, the vision of the “citizen engineer” is introduced by empowering citizens to become “active human sensors” at the source of defect detection and data collection, thus extending the role of citizens from passive infrastructure users to active infrastructure monitors. In the proposed method, volunteers are motivated and instructed to use mobile devices to capture and send geo-tagged images of defects (e.g. cracks, corrosion, trip/slip hazards, potholes, etc.) that they observe in an urban infrastructure environment, including a short description and severity rating. The collected photos and descriptions are processed using object recognition techniques. Defects are identified and extracted from the photos and quantified, whereas additional information about the defects and their perceived severity are obtained from the description field. Beyond the local condition measures, the aggregate data provides responsible authorities with a quantitative analysis of the detected defects as well as a measure of importance and severity (i.e. heat maps), as perceived by the citizen, that can be used to inform maintenance decisions. While the challenges of this framework are discussed in detail, it is expected to be a highly promising departure from the traditional top-down infrastructure monitoring approaches.