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Presents commentaries on the study "Application of Neural Networks (ANN) for Estimation of Concrete Strength," by Jong-In Kim et al, published in the May/June 2004 issue of the "Journal of Materials in Civil Engineering." Issues related to development of compressive strength of concrete; Issues related to ANN modeling of compressive strength of concrete; Issues related to some specific observation made by the authors.

The article offers information on the publication of the October 2014 issue of the journal "Computer-Aided Civil and Infrastructure Engineering (CACAIE)."

Cable is the most important component in cable-supported bridges and roof structures. Existing vibration methods for cable tension estimates are mainly based on measured acceleration responses. Such practice is relatively expensive and time-consuming due to the required installation of contact-type sensors and data acquisition systems. In this study, the novel noncontact vision-based sensor is proposed as an accurate and cost-effective alternative for the determination of cable tension forces. The noncontact measurement capacity of the vision sensor eliminates the need to access the cable to install conventional sensors, which may be highly difficult and risky. The proposed method is applied to measure cable forces for the cable-supported roof structure of the Hard Rock Stadium in Florida. To ensure that cable forces reach their design values, a series of field tests have been carried out during different roof erection stages. Satisfactory agreements are observed between measured cable forces by the vision-based sensor and the reference readings from load cells. The proposed method represents a convenient and low-cost method for either periodic or long-term monitoring of cable-supported structures. [ABSTRACT FROM AUTHOR]

Most of the existing methods for identification of vehicle axle loads are based on a model with known system parameters. In this study, a new method is proposed to simultaneously identify bridge structural parameters and vehicle dynamic axle loads of a vehicle–bridge interaction system from a limited number of response measurements. As an inverse output-only identification problem, the estimation of unknown axle loads is incorporated in the framework of an iterative parametric optimization process, wherein the objective is to minimize the error between the measured and predicted system responses. A Bayesian inference regularization is presented to solve the ill-posed least squares problem for input axle loads. Numerical analyses of a simply-supported single-span bridge and a three-span continuous bridge are conducted to investigate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the number of sensors, the measurement noise, and initial estimates of structural parameters on the accuracy of the identification results are investigated, demonstrating the robustness and efficiency of the proposed algorithm. Finally, it is shown that the bridge dynamic response can be accurately predicted using the identified axle load histories and structural parameters. [ABSTRACT FROM AUTHOR]

This paper presents a new computer vision-based method that simultaneously provides the moving vehicle's tire loads, the location of the loads on a bridge, and the bridge's response displacements, based on which the bridge's influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system. The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the co-authors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads. The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements. The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system's modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge's displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring. • Non-contact computer vision-based methodology for extracting displacement influence lines. • Simultaneous tracking of the bridge displacement, location and magnitude of the moving vehicle loads. • Experimental validation with a laboratory bridge system and pressurized vehicle model. • Excellent match between the simulated and experimental influence lines after model updating. [ABSTRACT FROM AUTHOR]

• Energy distribution characteristics of displacement and acceleration are revealed. • The estimated covariances on modal flexibility are used for comparison. • Lower uncertainty is obtained for modal parameters when using displacement. • Displacement response is more suitable for structural flexibility identification. For civil engineering structures, the dynamics-based structural health monitoring is commonly based on inverse analysis of structural acceleration response data to identify the structural parameters. As the measurement of structural displacement response becomes more feasible and cost-effective thanks to advances in computer vision techniques, a comparison of the displacement and acceleration response data for structural identification has become of considerable interest among researchers. This paper presents a comprehensive analysis and comparison of structural flexibility identification based on displacement versus acceleration response data through uncertainty quantification technique. Firstly, theoretical derivation reveals that the energy spectral density of acceleration response is equal to the fourth power of the frequency of the energy spectral density of displacement response, which affects the distribution of measurement noise across different frequency bands. Secondly, the uncertainty quantification method for modal flexibility from subspace-based system identification is utilized to compare the derivation process of covariance estimation on modal flexibility. Finally, numerical analysis and experimental example are carried out to compare the structural flexibility identification results when using the displacement versus acceleration. It shows that more precise identification results of lower-order modal parameters are obtained when using displacement, which further improves the precision of modal flexibility identification. The displacement is more suitable for identifying the modal flexibility when the measurement noises are at the same level. [ABSTRACT FROM AUTHOR]

Urban water delivery systems can be damaged by earthquakes or severely cold weather. In either case, the damage cannot easily be detected and located, especially immediately after the event. In recent years, real-time damage estimation and diagnosis of buried pipelines attracted much attention of researchers focusing on establishing the relationship between damage ratio (breaks per unit length of pipe) and ground motion, taking the soil condition into consideration. Due to the uncertainty and complexity of the parameters that affect the pipe damage mechanism, it is not easy to estimate the degree of physical damage only with a few numbers of parameters. As an alternative, this paper develops a methodology to detect and locate the damage in a water delivery system by monitoring water pressure on-line at some selected positions in the water delivery systems. For the purpose of on-line monitoring, emerging supervisory control and data acquisition technology can be well used. A neural network-based inverse analysis method is constructed for detecting the extent and location of damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water delivery system with one location of damage, and validated by using a set of data that have never been used in the training. It is found that the method provides a quick, effective, and practical way in which the damage sustained by a water delivery system can be detected and located. [ABSTRACT FROM AUTHOR]

The tiles that are used to protect the exterior of a building may separate from the concrete substrate and fall off as they age. To prevent accidents, a tiled wall can be inspected using various methods. In this study, nondestructive inspection (NDI) with microwave electromagnetic waves (EMWs) is used for detecting tile separation, and the chance of detecting the separation by this method is discussed in this paper. Here, the inspection apparatus is used to detect voids in concrete, including voids that simulate tile separation. An antenna attached to the apparatus transmits and receives microwaves to obtain a reflected intensity from the concrete. In addition, the distribution of reflected intensity is obtained using a proposed scanning method, multi-layered scanning. This involves several scans along the concrete surface at different antenna-to-surface distances. Typically, EMWs have to be analyzed with an expensive network analyzer in the time or frequency domain. However, we demonstrate that voids are detected from the obtained distribution of the reflected intensity of EMWs measured by a simple device without a network analyzer. The proposed method of NDI with EMWs is cheaper and simpler than conventional inspection methods. [ABSTRACT FROM AUTHOR]

Neural network-based strategies require balanced training datasets to avoid creating unreliable classification and prediction models. While these strategies are commonly used to model the dynamics of structural and mechanical systems, the imbalanced composition of monitoring data is a fundamental challenge for damage assessment in structural systems. The monitoring data often contain abundant observations from structures in their normal operating conditions (undamaged state) and small and partial information from systems in the damaged state. Therefore, the model, trained by adopting deep learning approaches, tends to show an ill-conditioned nature, limited to specific structures in a narrow range of damage conditions. The current study presents a damage assessment strategy that overcomes the limitations of unbalanced datasets. To improve the model's ability to distinguish between different health conditions, informative features are utilized to facilitate the differentiation of multiple classes according to the frequency content of vibration signals. The model acquires this ability by learning from a rich dataset of human voices (source domain), where low-level features that denote the vibration traits of human waveforms are extracted. Subsequently, this knowledge is transferred to the features of a target domain that has limited data for damage detection. The proposed methodology relies on creating an informative feature extractor training a Time-Delay Neural Network (TDNN) using a collection of human voice recordings. Cepstral and pitch features derived from the speech data are used as input features for the TDNN. This network is used to derive low-level features at intermediate layers of the network, called " x -vectors". These features store non-case-dependent information about the frequency content of the signals and depict the ability to distinguish between different classes according to a change in the frequency content of the investigated system. This is not a unique attribute of the original audio source domain, and it can be employed to help differentiate categories for any vibrating system where a modification in the frequency content is representative of a transition between classes, including the structural and mechanical systems. Because of the generalization trait of the x -vector, they can be employed to construct a Probabilistic Linear Discriminant Analysis model able to classify various damage classes considering vibration measurements obtained from a different system, i.e. , a structural system (target domain). Initially, the simulated acceleration response from the 12-degree of freedom structure are analyzed to affirm the effectiveness of the framework. Then, the method is further validated by using the field data of the Z24 bridge, to evaluate its reliability in real-world applications. [ABSTRACT FROM AUTHOR]

Summary: An innovative substructure approach is proposed for estimating the structural parameters of shear structures from the acceleration responses of individual substructures. Two parallel methods are created to form single‐degree‐of‐freedom models of each substructure. The behavioral characteristics of these substructure models chiefly depend on the structural parameters of the edges of the component substructure, which is separate from the shear structure. To obtain structural parameters from the substructure accelerations, discrete substructure models with accelerations are generated using Newmark's method and are found similar to the autoregressive moving average with exogenous (ARMAX) inputs models. Sophisticated techniques for solving ARMAX models are used to process the accelerations and to extract the structural parameters of the substructures. A linear relationship among model coefficients of the discrete substructure models and ARMAX models is discovered that provides an accurate and simple way to identify all the substructure parameters. A numerical simulation of a 10‐story shear structure during earthquake is performed to verify this substructure approach, where the factors of the size of the substructure and the noise disturbance are considered. Finally, this substructure approach is used to identify a structural model and reproduce the structural responses of a five‐story three‐dimensional structure in a shaking‐table experiment. [ABSTRACT FROM AUTHOR]

A novel numerical approach is presented, in the time domain, to simultaneously identify structural parameters and unmeasured input loadings using incomplete output measurement only. The identification problem is formulated as an optimization process, wherein the objective function is defined as the discrepancy between the measured and the predicted data, and is solved by a damped Gauss-Newton method. Because the proposed algorithm is a time domain technique, forward analyses are required to obtain predicted system responses so as to compute the discrepancy. Therefore, we propose an input force estimation scheme in the identification process to complete the task of input-output forward analyses, for the case of output-only measurement. The relationship between the unknown input loadings and the output measurement is established through a state space model, which basically formulates an ill-posed least squares problem. A statistical Bayesian inference-based regularization technique is presented to solve such a least squares problem. Finally, the proposed approach is illustrated by both numerical and experimental examples using output-only measurements of either acceleration or strain time histories. The results clearly show the robustness and the applicability of the proposed algorithm to simultaneously identify structural parameters and unmeasured input loadings with a high accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Application of carbon fiber-reinforced polymer (CFRP) is one of the effective strengthening methods for structural members such as reinforced concrete columns and beams. However, air voids and debonds between CFRP and concrete due to poor workmanship may degrade the structural performance otherwise expected by the strengthening. In order to minimize such debonds and ease the installation, the authors propose to wrap compressive concrete members with narrow strips of CFRP laminates with spacing. This paper focuses on an experimental study to investigate the effectiveness of applying the narrow strips of CFRP laminates. In this study, 60 concrete cylinders wrapped with CFRP strips having different spacings and widths are tested under compression load. The effects of several key parameters such as spacing, spliced length, number of layers, and section area of the CFRP laminates are investigated. In addition, stress–strain curves of pre-damaged specimens wrapped with CFRP laminates are also focused. Based on the experimental results, constitutive models of concrete confined by narrow strips of CFRP laminates are proposed. [Copyright &y& Elsevier]

Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.