Pavement Acceptance Testing: Risk-Controlled Sampling Strategy

Acceptance testing is a critical aspect of the quality control and quality assurance (QC/QA) program to ensure the reliable long-term performance of pavement. A typical acceptance testing specification includes acceptable quality characteristics (AQCs), testing methods, number of samples, sample locations, and acceptance criteria. In the current practice, Indiana Department of Transportation (INDOT) accepts pavement by sampling and testing materials with a pre-determined, very low frequency at random locations, leading to a significant problem: testing results are not truly “representative” of the project because sampling is neither based on a statistical foundation, nor on the reliability concept. This study developed a systematic guideline that has addressed the aforementioned problem of material acceptance testing in four aspects: identifying key material properties for testing, selecting sample locations, designing acceptance criteria, and determining optimal sample size. Key material properties that are critical to the pavement long-term performance are identified by comparing with sensitive material properties in MEPDG. A random sampling mechanism was devised based on two spatial indices to control the spatial pattern of samples to minimize the influence from spatial autocorrelation. Risk-based acceptance criteria was proposed based on statistical methods to control the agency’s risk at a desired level given a specific sampling and testing strategy, based on which optimal sample size is determined from a risk perspective. Cost analysis approaches were developed to estimate the total cost of acceptance testing by integrating the risk of making incorrect decision and enable the determination of optimal sample size from a cost perspective. Additionally, quality control chart was exploited as a complementary tool to ensure the consistency of the pavement quality of a project. The results of this study were validated using real data from INDOT projects, and a web tool that incorporates the newly created methods in this study was developed to assist the field pavement QA practice

Data-Driven Approach to Holistic Situational Awareness in Construction Site Safety Management

The motivation for this research stems from the promise of coupling multi-sensory systems and advanced data analytics to enhance holistic situational awareness and thus prevent fatal accidents in the construction industry. The construction industry is one of the most dangerous industries in the U.S. and worldwide. Occupational Safety and Health Administration (OSHA) reports that the construction sector employs only 5% of the U.S. workforce, but accounts for 21.1% (1,008 deaths) of the total worker fatalities in 2018. The struck-by accident is one of the leading causes and it alone led to 804 fatalities between 2011 and 2015. A critical contributing factor to struck-by accidents is the lack of holistic situational awareness, attributed to the complex and dynamic nature of the construction environment. In the context of construction site safety, situational awareness consists of three progressive levels: perception – to perceive the status of construction entities on the jobsites, comprehension – to understand the ongoing construction activities and interactions among entities, and projection – to predict the future status of entities on the dynamic jobsites. In this dissertation, holistic situational awareness refers to the achievement at all three levels. It is critical because with the absence of holistic situational awareness, construction workers may not be able to correctly recognize the potential hazards and predict the severe consequences, either of which will pose workers in great danger and may result in construction accidents. While existing studies have been successful, at least partially, in improving the perception of real-time states on construction sites such as locations and movements of jobsite entities, they overlook the capability of understanding the jobsite context and predicting entity behavior (i.e., movement) to develop the holistic situational awareness. This presents a missed opportunity to eliminate construction accidents and save hundreds of lives every year. Therefore, there is a critical need for developing holistic situational awareness of the complex and dynamic construction sites by accurately perceiving states of individual entities, understanding the jobsite contexts, and predicting entity movements. The overarching goal of this research is to minimize the risk of struck-by accidents on construction jobsite by enhancing the holistic situational awareness of the unstructured and dynamic construction environment through a novel data-driven approach. Towards that end, three fundamental knowledge gaps/challenges have been identified and each of them is addressed in a specific objective in this research. The first knowledge gap is the lack of methods in fusing heterogeneous data from multimodal sensors to accurately perceive the dynamic states of construction entities. The congested and dynamic nature of construction sites has posed great challenges such as signal interference and line of sight occlusion to a single mode of sensor that is bounded by its own limitation in perceiving the site dynamics. The research hypothesis is that combining data of multimodal sensors that serve as mutual complementation achieves improved accuracy in perceiving dynamic states of construction entities

Discovering Spatio-Temporal Co-Occurrence Patterns of Crimes with Uncertain Occurrence Time

The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is often uncertain due to a lack of witnesses. This occurrence time uncertainty further results in the uncertainty of the spatio-temporal neighborhood relationships and STCPs. Existing methods have mostly modeled the uncertainty of events under the independent and identically distributed assumption and utilized one-sided distance information to measure the distance between uncertain events. As a result, STCPs detected from a dataset with occurrence time uncertainty (USTCPs) are likely to be erroneously assessed. Therefore, this paper proposes a probabilistic-distance-based USTCP discovery method. First, the temporal probability density functions of crime events with uncertain occurrence times are estimated by considering the temporal dependence. Second, the spatio-temporal neighborhood relationships are constructed based on the spatial Euclidean distance and the proposed temporal probabilistic distance. Finally, the prevalent USTCPs are identified. Experimental comparisons performed on twelve types of crimes from X City Public Security Bureau in China demonstrate that the proposed method can more objectively express the occurrence time of crimes and more reliably identify USTCPs

Discovery of co-location patterns based on natural neighborhood

Discovery of co-location patterns is crucial to understanding the interaction among different spatial features. The construction of neighborhood relationship among spatial features plays a key role in co-location pattern mining, however, existing methods are difficult to construct appropriate neighborhood relationship when the spatial features distribute unevenly.This limitation is very likely to make the omission and/or misjudgment of co-location patterns.To address this issue, a co-location pattern mining method based on natural neighborhood is proposed in this study.After removing the randomly distributed spatial features,natural neighborhood relationship among different spatial features is adaptively constructed on basis of three principles, i.e. geographic proximity, the consistency of density and compactness of neighboring relationship. The multi-level co-location patterns are discovered based on the delaunay triangulation network. The experimental results showed that the proposed method could discover the co-location patterns among unevenly distributed spatial features completely and accurately, and no user-specified parameters are required for the construction of natural neighborhood

Discovering Spatio-Temporal Co-Occurrence Patterns of Crimes with Uncertain Occurrence Time

The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is often uncertain due to a lack of witnesses. This occurrence time uncertainty further results in the uncertainty of the spatio-temporal neighborhood relationships and STCPs. Existing methods have mostly modeled the uncertainty of events under the independent and identically distributed assumption and utilized one-sided distance information to measure the distance between uncertain events. As a result, STCPs detected from a dataset with occurrence time uncertainty (USTCPs) are likely to be erroneously assessed. Therefore, this paper proposes a probabilistic-distance-based USTCP discovery method. First, the temporal probability density functions of crime events with uncertain occurrence times are estimated by considering the temporal dependence. Second, the spatio-temporal neighborhood relationships are constructed based on the spatial Euclidean distance and the proposed temporal probabilistic distance. Finally, the prevalent USTCPs are identified. Experimental comparisons performed on twelve types of crimes from X City Public Security Bureau in China demonstrate that the proposed method can more objectively express the occurrence time of crimes and more reliably identify USTCPs

Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images

In this study, we combined ground-based hyperspectral data, unmanned aerial vehicles (UAVs) remotely sensed hyperspectral images, and 1D-CNN algorithms to quantitatively characterize and estimate the Chemical Oxygen Demand (COD) of estuarine urban rivers. The spectral response mechanism of COD is imprecise due to its complex composition; however, we found that hyperspectral remote sensing data could be used for COD monitoring because of the data's rich spectral information. The potential of hyperspectral sensors installed on UAVs to estimate and map the COD of urban rivers has not been thoroughly explored. We used in situ above water hyperspectral data from 498 sites and synchronous water samples in band ratio, SVM, and 1D-CNN algorithms to build retrieval models. We found that the 1D-CNN model performed the best with an R-2 of 0.78 and an RMSE of 5.22 when using the original reflectance data as input. The 1D-CNN model may also have a better ability to identify water samples with abnormally high concentrations. Our results revealed that transferring the ground-based derived 1D-CNN retrieval model for COD to the high-resolution hyperspectral images is a reliable method for determining COD from the images. We concluded that UAV remotely sensed hyperspectral images are valuable for COD concentration monitoring and mapping, critical to urban water quality management decision making

A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection

A neural network-based object detection algorithm has the advantages of high accuracy and end-to-end processing, and it has been widely used in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation of ship targets, the complex background of near-shore scenes, and the dense arrangement of some ships make it difficult to improve detection accuracy. To solve the above problem, in this paper, a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection is proposed, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. The SCSA module uses the features at each scale output from the backbone to calculate where the network needs attention in space and enhances the features of the feature pyramid network (FPN) output to eliminate interference from noise, and land complex backgrounds. In addition, this paper analyzes the reasons for the “score shift” problem caused by average precision loss (AP loss) and proposes the global average precision loss (GAP loss) to solve the “score shift” problem. GAP loss enables the network to distinguish positive samples and negative samples faster than focal loss and AP loss, and achieve higher accuracy. Finally, we validate and illustrate the effectiveness of the proposed method by performing it on SAR Ship Detection Dataset (SSDD), SAR-ship-dataset, and High-Resolution SAR Images Dataset (HRSID). The experimental results show that the proposed method can significantly reduce the interference of background noise on the ship detection results, improve the detection accuracy, and achieve superior results to the existing methods

An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns

Mining spatial co-location patterns plays a key role in spatial data mining. Spatial co-location patterns refer to subsets of features whose objects are frequently located in close geographic proximity. Due to spatial heterogeneity, spatial co-location patterns are usually not the same across geographic space. However, existing methods are mainly designed to discover global spatial co-location patterns, and not suitable for detecting regional spatial co-location patterns. On that account, an adaptive method for mining hierarchical spatial co-location patterns is proposed in this paper. Firstly, global spatial co-location patterns are detected and other non-prevalent co-location patterns are identified as candidate regional co-location patterns. Then, for each candidate pattern, adaptive spatial clustering method is used to delineate localities of that pattern in the study area, and participation ratio is utilized to measure the prevalence of the candidate co-location pattern. Finally, an overlap operation is developed to deduce localities of (k+1)-size co-location patterns from localities of k-size co-location patterns. Experiments on both simulated and real-life datasets show that the proposed method is effective for detecting hierarchical spatial co-location patterns

Green Synthesis of Flowerball-like MoS₂/VC Nanocomposite and Its Efficient Catalytic Performance for Oxygen Reduction Either in Alkaline or Acid Media

Opening up electrocatalysts for oxygen reduction reaction (ORR) is essential for practical application in fuel cells and metal-air batteries; however, how to make the catalysts with both good performance and low cost is difficult. Recently, research on the ORR of molybdenum disulfide-based catalysts in alkaline electrolytes has been on the rise. However, the development of MoS2 catalyst for acidic ORR is still in its infancy. Herein, without using reductant and morphology control reagent, we firstly obtained flowerball-like MoS2/Vulcan XC-72R (VC) nanocomposites via hydrothermal method. The designed composite exhibits a nearly 4e− ORR process with 0.78 and 0.92 V onset potentials in 0.1 M KOH and HClO4, respectively. Furthermore, the flowerball-like composite shows utmost electrochemical stability judging by 87 and 80% current retention for about 5.5 h either in alkaline or acid media, long term durability for continuous 10,000 cycles, and stronger resistance to methanol than the commercial Pt/C catalyst. The abundant Mo edges as catalytic active centers of flowerball-like structure, high electron conductivity, and enhanced mass transport in either alkaline or acidic electrolyte are favorable for catalytic performance. The prepared catalyst provides great potential for the substitution of noble metal based catalysts in fuel cells and metal-air batteries