Improving quality control through chance constrained programming: A case study using Bailey method

Rutting in bituminous pavements predominantly depends on the aggregate gradation. In particular, for fine graded mixtures, the aggregate gradation below 2.36 mm play a critical role since more than 60% of aggregates will pass through this sieve. The present work is focused on quantifying the rut resistance of three different fine aggregate gradations. The control gradation is the well-graded gradation used in India, and the other two gradations were obtained following Bailey method. The Bailey gradations were designed in such a way that the gradation below 2.36 mm sieve size was varied while the gradation above this sieve size was kept the same as that of the control gradation. The mixtures were fabricated using an unmodified binder (binder content kept constant at 5.4%) at 6% air voids using a shear box compactor. The influence of aggregate gradation on the creep and recovery response of bituminous mixtures was investigated at two temperatures by applying 10,000 repetitions of a repeated trapezoidal loading and recovery cycles. From the resulting residual strain curve, the Bailey mixture designed towards the coarser side below 2.36 mm sieve was found to have a higher resistance to rutting when compared to the conventional bituminous concrete-middle gradation.

The dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures’ aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PDF

Air voids in an asphalt concrete pavement highly influences its performance. Careful consideration of void requirements in compacted bituminous mixtures is more important than stability consideration, since the latter is always higher than necessary. Lack of attention to void requirements may be a direct cause of instability of asphalt concrete pavements. The present work studied the influence of aggregate type and its gradation on the voids in mineral aggregate and air voids of asphalt concrete paving mixtures as compacted according to the Marshall method. Three types of aggregates and five different gradations were employed. Aggregate gradation may be used to control the void requirements in a specific asphalt concrete mixture, influencing and/or affecting other important engineering properties such as water permeability of that specific mixture. On the other hand, aggregate type can provide the necessary voids requirements for an aggregate-asphalt mixture without sacrificing other important engineering properties. Two of the aggregates easily satisfied the void requirements. The third aggregate could not satisfy the same void requirements unless a porous gradation was used. Porous mixtures have more air voids, resulting in an increased permeability to water. This increase in permeability is not the same for different aggregate gradations. The coarser the mix, the higher the rate of increase in the water permeability for the same type of aggregate.

The objective of this paper is to provide information from multiple perspectives on the current state of the practice with regard to using recycled materials and recycling agents (RAs) in asphalt concrete mixtures. This information was collected through a survey of U.S. transportation agencies and RA suppliers combined with a search of RA-related specifications and pilot projects previously constructed. Moreover, a case study describing the Virginia Department of Transportation’s experience with RAs provides a tangible example of how at least one agency is approaching the potential implementation of these technologies. This practice review was achieved by documenting the experience, lessons learned, and best practices of multiple asphalt experienced contractors and asphalt binder suppliers in the Virginia area. This paper follows a similar survey conducted in 2014 as part of NCHRP 09-58 and provides a second look at the use of RAs across North America. Not all state departments of transportation have experience with using RAs. Factors preventing the use of RAs included specification limitations, lack of expertise in processing recycled materials, supporting data, and negative prior experiences. Developing a performance-based testing framework is mandatory for the successful use of RAs. In general, good and frequent communication with the RA supplier is critical and necessary during the planning stages, the production of mixtures, and the continuous quality control by the supplier to resolve issues when they arise. Finally, a strong quality control and quality assurance-testing program should be implemented to ensure that materials meet the properties needed to produce a good-performing mixture.

The selection of the aggregate gradation, in the process of the asphalt mix design, is one of the most critical steps because it accounts for the overall strength of the asphalt mixture in terms of resisting the permanent deformation or rutting. This paper focuses on evaluating the current aggregate gradation selection procedure for the hot asphalt mix (HAM) using the three Bailey ratios. Additionally, this study analyzes, theoretically, the compaction and performance characteristics of the resulting HAM designed using the traditional design procedures which follow the trial-and-error technique in order to have a mix that satisfies the specification range. Results show that 14% of the samples prepared using the traditional technique satisfy the Bailey method guidelines and thus indicate good performance in the field. However, almost 80% of the asphalt mixes are tender asphalt mixes that are prone to segregation in the field, and 6 to 7% of the asphalt mixes are hard to compact. Based on the findings of this research, it is recommended that the Bailey Method analysis process should be incorporated into the mix design process as an additional tool to develop and select trial blends for the design of the asphalt mixes in Egypt.

Quality Control Management and Communication Between Radiologists and Technologists

Mixture properties (aggregate gradation and volumetric quantities), rate of loading and environmental conditions are the most important factors that affect the |E*| values. The main objective of this study was to develop a rational approach to investigate and model the effect of air voids and asphalt content on the |E*| master curves and consequently predict pavement performance. In this study, |E*| tests were conducted on three asphalt concrete mixtures with the same aggregate gradation, but different binder grades. For each of these mixtures, the air void and asphalt contents were varied at three levels. It is found that the developed method provides a more accurate estimate of the effects of volumetric changes in hot mix asphalt. The application of the proposed approach would be most beneficial for quality control/quality assurance purposes, performance-related specifications and for estimating contractors' incentives and penalties, where |E*| is utilised to predict the pavement performance.

This research documents an extensive study on the design and characterization of asphalt mixtures for use as road pavement material. Several aspects of asphalt mixtures were addressed using the state of the art laboratory test equipment and technical literature from different information sources. The research was divided into two phases. Phase one included the design and detailed analysis of compaction and performance characteristics of asphalt concrete mixtures with aggregate structures that were designed using an analytical method of aggregate blending. Three aggregate types were considered in this study: limestone, sandstone, and granite. All the aggregates were crushed aggregates. Three different aggregate structures were designed for each aggregate type using the Bailey method of aggregate gradation evaluation. The Bailey method is a comprehensive gradation. Sandstone and Granite mixtures had a nominal maximum aggregate size (NMAS) of 12.5mm and were designed for high traffic level, while two types of Limestone mixtures were designed (25.4 mm and 12.5 mm NMAS) for two traffic levels (high and low traffic volumes). For the heavy traffic mixtures the binder type selected was PG 76-22M while PG70-22 was used for low volume mixtures. The outcome of this research suggests that suitable mixes can be developed with dense aggregate structures using the Bailey method of aggregate gradation that provides good resistance to permanent deformation while still maintaining adequate levels of durability. A systematic, simplified design approach was recommended in which asphalt mixtures are designed based on the locking point concept, analytical aggregate gradation method and fundamental mechanistic properties that describe the behavior of asphalt mixtures based on sound engineering principles.

Next-generation sequencing of large numbers of individuals presents challenges in data preparation, quality control, and statistical analysis because of the rarity of the variants. The Genetic Analysis Workshop 17 (GAW17) data provide an opportunity to survey existing methods and compare these methods with novel ones. Specifically, the GAW17 Group 2 contributors investigate existing and newly proposed methods and study design strategies to identify rare variants, predict functional variants, and/or examine quality control. We introduce the eight Group 2 papers, summarize their approaches, and discuss their strengths and weaknesses. For these investigations, some groups used only the genotype data, whereas others also used the simulated phenotype data. Although the eight Group 2 contributions covered a wide variety of topics under the general idea of identifying rare variants, they can be grouped into three broad categories according to their common research interests: functionality of variants and quality control issues, family-based analyses, and association analyses of unrelated individuals. The aims of the first subgroup were quite different. These were population structure analyses that used rare variants to predict functionality and examine the accuracy of genotype calls. The aims of the family-based analyses were to select which families should be sequenced and to identify high-risk pedigrees; the aim of the association analyses was to identify variants or genes with regression-based methods. However, power to detect associations was low in all three association studies. Thus this work shows opportunities for incorporating rare variants into the genetic and statistical analyses of common diseases.

The users of soil-cement in the water resources application have broadened over the past years. Still one construction related task has remained essentially the same: quality control. Soil-cement is a highly compacted mixture of soil; usually sand, Portland cement, and water. It has the properties of a low-strength concrete. Without adequate controls on the quality of the materials and construction methods, the performance of the project may not meet expectations. A successful soil-cement project begins with the development of a quality control program with the responsibilities of each party clearly defined. The quality control plan should address submittals which must receive approval from the project engineer; mixture proportioning considerations; minimum requirements for equipment such as the mixing plant, compactors, and curing equipment; types and frequencies of material testing; and acceptance criteria for construction procedures and individual material tests. Quality control practices during construction do hot have to negatively impact the contractor's production. Each member of the project team, from the laborer to the project manager, can be aware and trained in his or her responsibilities to ensure a quality product. The benefits of having everyone on the project buy into and execute their responsibilities will minimize any rework and ensure that the performance criteria will be met or exceeded. This paper will focus on quality control issues where soil-cement is used for bank stabilization grade control structures and slope protection. Issues involving mixing of soil-cement will be directed at central plant pugmills.

BackgroundColorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.Methods and findingsOur whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7–10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17–30 times the number of potential human errors were detected within an average of 1.2 days.ConclusionsThe AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.

Performance measures play a critical role in the operation of Intelligent Transportation Systems (ITS), because they provide feedback to the operators regarding system operations and efficiency. Traffic management systems use archived data, provided by various sensors, as a basis for describing normal conditions and predicting traffic conditions that may be expected at a particular time and place in the highway system. However, data quality is one of the principal concerns of archived ITS data users for the following reasons: 1) Manual inspection techniques are unable to detect significant errors because of the large volume of ITS data; 2) Only minimal error detection can be performed as the data is being collected; 3) Sensors may only fail intermittently and not affect long-term averages; 4) Sensor failures may be masked by congestion or incidents. This project examined several quality control issues on one of the nation’s busiest sections of Interstate, the Borman expressway (I-80/94). Various tests such as speed and volume comparisons, data availability and average effective vehicle length test were developed and applied, revealing significant inconsistency in data quality provided by different detection technologies. Results that document these problems are presented in this study and causes of non-random changes are identified. Finally, several recommendations for improving construction and configuration procedures are proposed. Several simple performance metrics are also documented that transportation agencies can use to assess the quality of traffic data and sustain that quality over time.

Molecular testing in non-small cell lung cancer (NSCLC) identifies patients likely to respond to targeted therapeutics. Panel-based testing often employs next-generation sequencing, but challenges include high sample failure rates, quality control issues, high tissue requirements and long turnaround times. Significant proportions of patients do not receive appropriate targeted therapy, with inferior clinical outcomes. Aspyre Lung is a targeted genomic profiling assay for 114 actionable or prognostic genomic variants across 11 genes with a two-day turnaround time of specimen to result. We profiled 198 NSCLC patient tissue samples using Aspyre Lung and a next-generation sequencing- (NGS)-based assay. Cohort A comprised 107 samples that failed to inform due to NGS quality checks, and Cohort B 91 samples that underwent successful NGS testing. Results were compared, and discrepancies resolved by orthogonal methods. For Cohort A (NGS fails), 103 (96 %) passed Aspyre Lung quality control, successfully yielding genomic results, including 48 (47 %) samples with ≥ one variant. In Cohort B (NGS pass), all samples passed Aspyre quality control with 97 % concordance to NGS-based testing. Notably, 80 % EGFR variant-positive samples were stages I and II. Aspyre Lung profiled 96 % of samples where NGS-based methods failed, and uncovered variants in samples successfully tested by NGS and deemed negative. Aspyre Lung detected ALK and EGFR variants from patients with early-stage disease, demonstrating utility as a rapid screening assay prior to neoadjuvant immuno-chemotherapy consideration.

Gene expression array technologies are rapidly emerging for use in various genome-wide studies in biology and medicine (1)(2)(3). Results from the recently completed sequencing of the human genome predict that the number of genes in the human genome is much smaller (∼33 000 genes) than anticipated (4). This implies that the major complexity in biological mechanisms in humans lies in the synergistic effects among various genes. Therefore, to decipher the secrets of our life and, ultimately, to find the cures for many human diseases, biologists must deal with multiple genes and their interactive transcripts simultaneously. High-throughput biotechnologies, including gene chip approaches, will play an important role in these studies (5). However, quality control over thousands of gene expression values and full utilization of the information from these high-throughput data are extremely difficult, and important issues in quality control and bioinformatic approaches have not been resolved (6)(7). A series of careful analyses on the variability of array instrumentation and on the statistical evaluation of gene expression intensities are required for reliable and consistent inference on gene chip data. Specifically, sources of error and their confidence levels on these high-throughput measurements need to be better understood because some can significantly alter our inferences and conclusions (8). The purpose of this report is to dispel three common misconceptions about array experiments. It often is believed that reproducibility of gene expression data is high enough to perform bioinformatic discovery without a replicated chip experiment and that duplicated array data are necessary only for confirmation of such a discovery, which may help publication in high-standard journals. Investigators who have successfully obtained resources for high-cost gene chip experiments must optimize their experimental strategy for their study goals within limited resources. Therefore, for an initial study, single-chip experiments for each …

The present study seeks to demonstrate the feasibility of avoiding the preliminary phase, which is mandatory in all conventional approaches for internal quality control (IQC) management. Apart from savings on the resources consumed by the preliminary phase, the alternative approach described here is able to detect any analytic problems during the startup and provide a foundation for subsequent conventional assessment. A new dynamically updated predictive control chart (PCC) is used. Being Bayesian in concept, it utilizes available prior information. The manufacturer's prior quality control target value, the manufacturer's maximum acceptable interassay coefficient of variation value and the interassay standard deviation value defined during method validation in each laboratory, allow online IQC management. An Excel template, downloadable from journal website, allows easy implementation of this alternative approach in any laboratory. In the practical case of prothrombin percentage measurement, PCC gave no false alarms with respect to the 1ks rule (with same 5% false-alarm probability on a single control sample) during an overlap phase between two IQC batches. Moreover, PCCs were as effective as the 1ks rule in detecting increases in both random and systematic error after the minimal preliminary phase required by medical biology guidelines. PCCs can improve efficiency in medical biology laboratories.