•IMTR regularizes noisy influencelines for damage characterization in beams.•TR and IMTR compared and implementedon analytical and experimental data.•IMTR improves performance over TR in cases of ...deep, sharp damage.
Bridge structures decay throughout their lives even under nominal operating conditions. As bridge infrastructure ages and wears naturally or under extreme load, there is a need to monitor and evaluate bridge performance in an efficient way. This paper presents an impairment detection method that assesses the curvature of noisy static deformation influence lines to predict the location and severity of structural damage. In this method, a parametric approximation and two direct regularization methods i.e., Tikhonov Regularization (TR) and the proposed Iterative Multi-parameter Tikhonov Regularization (IMTR), are implemented to reduce the impact of measurement noise on flexural rigidity estimations. While the TR method assumes one regularization parameter for all unknowns of the optimization problem, the IMTR method has individual, iteratively optimized, regularization parameters for each unknown. To evaluate the performance of the presented method, four nominally identical beam structures with four different damage scenarios and multiple levels of measurement noise are studied. Combining the quadratic spline parametric approximation with either direct regularization method produces adequate curvature estimates that are subsequently used to predict the location and severity of damage. In cases of deep, sharp damage, the IMTR method improves the prediction performance by reducing percent error 4–32%, for noise levels ranging from 0% to 5%, when compared to prediction results from the conventional TR. Both regularization methods give comparable results for shallow, wide damage. A laboratory experiment is included that presents the FRE on a statically indeterminate system; both TR and IMTR provide reasonable estimations of the location and severity of damage.
Probabilistic neural networks (PNNs) are artificial neural network algorithms widely used in pattern recognition and classification problems. In the traditional PNN algorithm, the probability density ...function (PDF) is approximated using the entire training dataset for each class. In some complex datasets, classmate clusters may be located far from each other and these distances between clusters may cause a reduction in the correct class's posterior probability and lead to misclassification. This paper presents a novel PNN algorithm, the competitive probabilistic neural network (CPNN). In the CPNN, a competitive layer ranks kernels for each class and an optimum fraction of kernels are selected to estimate the class-conditional probability. Using a stratified, repeated, random subsampling cross-validation procedure and 9 benchmark classification datasets, CPNN is compared to both traditional PNN and the state of the art (e.g. enhanced probabilistic neural network, EPNN). These datasets are examined with and without noise and the algorithm is evaluated with several ratios of training to testing data. In all datasets (225 simulation categories), performance percentages of both CPNN and EPNN are greater than or equivalent to that of the traditional PNN; in 73% of simulation categories, the CPNN analyses show modest improvement in performance over the state of the art.
Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and ...the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.
Bridge wear and deterioration occur over time under typical operations. Inspections can be resource intensive, infrequent, and sometimes require bridge closure. Instrumented passing vehicles may be ...used to record vibration responses and extract damage‐sensitive bridge frequency response characteristics. This paper presents a methodology for extracting bridge frequencies from crowdsourced dynamic response data recorded within passing vehicles. Detected bridge frequency changes and a novel point cloud methodology are used to identify critical damage scenarios and estimate remaining life. The use of a novel crowdsourced Welch methodology allows for spectra averaging that reduces the influence of noise and transient events. Numerical and experimental studies illustrate that bridge frequencies can be accurately estimated and that changes in bridge frequency caused by different damage scenarios can be characterized. Numerical examples for the damage scenario analysis, including estimation of remaining life, are provided for two bridge types and three specific damage scenarios.
Lithic raw material differences are widely assumed to be a major determining factor of differences in stone tool morphology seen across archaeological sites, but the security of this assumption ...remains largely untested. Two different sets of raw material properties are thought to influence artifact form. The first set is internal, and related to mechanical flaking properties. The second set is external, namely the form (size, shape, presence of cortex) of the initial nodule or blank from which flakes are struck. We conducted a replication experiment designed to determine whether handaxe morphology was influenced by raw materials of demonstrably different internal and external properties: flint, basalt, and obsidian. The knapper was instructed to copy a “target” model handaxe, produced by a different knapper, 35 times in each toolstone type (n = 105 handaxes). On each experimental handaxe, 29 size-adjusted (scale-free) morphometric variables were recorded to capture the overall shape of each handaxe in order to compare them statistically to the model. Both Principal Components Analysis (PCA) and a Multivariate Analysis of Variance (MANOVA) were used to determine if raw material properties were a primary determinate of patterns of overall shape differences across the toolstone groups. The PCA results demonstrated that variation in all three toolstones was distributed evenly around the model target form. The MANOVA of all 29 size-adjusted variables, using two different tests, showed no statistically significant differences in overall shape patterns between the three groups of raw material. In sum, our results show that assuming the primacy of raw material differences as the predominant explanatory factor in stone tool morphology, or variation between assemblages, is unwarranted.
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•Lithic raw material differences are assumed to determine stone tool morphology.•We conducted an experiment testing the influence of raw material on handaxe shape.•We examined three demonstrably different stone types: flint, basalt, and obsidian.•Stone tool plan-view and profile-view shape is not determined by raw material type.•Raw material differences as a predominant explanatory factor is unwarranted.
•Effect of a unique CEB geometry on strength testing methods.•Effect of mix designs on CEB flexural and compressive strength.•CEBs with optimized mix designs surpass ASTM strength requirements.
...Compressed earth blocks (CEBs) are masonry units that combine soil, stabilizer, and water under pressure to form an earth block. Unit block performance is dependent on the characteristics of soil and the mix design. This paper presents CEB unit strength test methods and results for CEBs produced from 14 mix designs at 7 and 28 day curing times in both saturated and unsaturated conditions. Trends in the effect of mix design on block strength reflect that strength increases with both moisture and cement content in the regimes of applicability for the production machinery. The unsaturated, 28-day unit compressive strengths ranged from 4.92 MPa to 15.72 MPa. The optimal mix contained 10.91% cement and 11.40% water which resulted in an average compressive strength of 15.15 MPa; ASTM C90 mandates a minimum compressive strength of 13.79 MPa.
Structural degradation is an inevitable part of a structure’s service life. Detecting structural impairments and assessing their nature is a significant challenge. Degradations reduce structural ...system stiffness and subsequently affect system deformations. An appropriate structural health index that is able to capture these changes in deformation and relate them to a structural system stiffness may help engineers to adequately rate structural condition. This paper outlines a theoretical framework for the utilization of static deformation influence lines for estimating the flexural rigidity of Euler-Bernoulli beams. In the proposed technique, the relationship between the second derivative of the deformation influence line and the flexural rigidity for both statically determinate and indeterminate beam structures is presented. The proposed method provides a flexural rigidity estimate (FRE) over the entire span that is based on a single measurement location and estimates both the location and severity of impairments, regardless of the location of the measurement or the damaged zones. Noisy analytical simulations are presented with noise levels of 0%, 0.5%, 1%, 2%, 3%, and 4%; in all cases the modeled damage is quantified and localized using the FRE. A laboratory experiment is also presented that validates the theoretical framework.
•Features are extracted from raw bridge acceleration data from 8 bridges to provide network training data; no FE models are necessary for training.•Multiple neural network voting ensemble ...configurations are presented.•Impact detection ranges from 91 −100% while average false positive rates are 0.00–0.75%.
Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Railroads represent important infrastructure components of the transportation sector and provide both passenger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operating conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which comprises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%.
Bridges are susceptible to deterioration and damage as they age and should be routinely assessed to evaluate their integrity and safety for service. Traditionally, structural monitoring has comprised ...visual inspections, however this is both time and labor intensive. Researchers have shown that sensors on moving vehicles may provide insight into the dynamic behavior of bridges. Accelerometers within smartphones may serve as the sensors from which data is collected; thus, enabling massive data collection from a fleet of potential monitoring vehicles. This paper presents four postprocessing strategies for estimating bridge frequencies from smartphone acceleration data streams with no a priori information about the mass or stiffness of the bridge or vehicle. These techniques utilize the DFT and MUSIC algorithms to calculate vehicle acceleration frequency spectrums from which the fundamental bridge vibration frequency may be estimated. Both single-vehicle and crowdsourced postprocessing techniques are investigated. Utilizing the MUSIC algorithm within a crowdsourcing framework, the correct bridge frequency was identified in all analytical simulations within 4% error, representing a significant increase in performance over single-vehicle estimations made using MUSIC. The effect of user interaction with the smartphone is studied by including superimposed acceleration signals on 25–100% of analytical results; the superimposed user events included a dropped smartphone and talking on a smartphone. Increasing the percentage of noisy signals in the pool of evaluated accelerations generally reduces performance with the exception of crowdsourced estimations made using the MUSIC algorithm, which proved to be robust against user interaction with the smartphone.
Impairments that occur in structural systems may degrade performance or prevent a structure from functioning safely. Detecting impairments prior to a structural failure reduces the effect of ...impairments on the safety of those using a structure. This article describes the computational framework of a Structural Impairment Detection System (SIDS) that processes the digital data streams of electronic sensors attached to critical components of a structure. The resulting system comprises a competitive array of neural networks that can accurately describe the types and severity of likely impairments present in the structure. The competitive array of neural networks is trained to detect patterns in data streams specific to likely target impairments. Data streams generated from ABAQUS models and from electronic sensors are used in training and evaluating the system. As part of the initial development of this SIDS, the counterweight truss of a 100‐year old railroad drawbridge was instrumented and evaluated. The resulting data streams were diagnosed autonomously by the SIDS as being similar to one of two operational conditions: unimpaired or a single impairment present in a member embedded within the counterweight. Further investigation of the counterweight truss is underway to verify the accuracy of the assessments.
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