Automatic Error Detection Research Articles

An approach to automatic process deviation detection in a time-critical clinical process.

Prior research has shown that minor errors and deviations from recommended guidelines in complex medical processes can accumulate to increase the likelihood that a major error will go uncorrected and lead to an adverse outcome. Real-time automatic and accurate detection of process deviations may help medical teams better prevent or mitigate the effect of errors and improve patient outcomes. Our goal was to develop an approach for automatic detection of errors and process deviations in trauma resuscitation. Using video review, we coded activity traces of 95 pediatric trauma resuscitations collected in a Level 1 trauma center over two years (2014-2016). Twenty-four randomly selected activity traces were compared with a knowledge-driven model of trauma resuscitation workflow using a phase-based conformance checking algorithm for detecting true and false deviations (alarms). An analysis of false alarms identified three types of causes: (1) model gaps or discrepancies between the model ("work as imagined") and actual practice ("work as done"), (2) errors in activity traces coding, and (3) algorithm limitations. We repaired the system to remove model gaps, reduce coding errors, and address algorithm limitations. The repaired system was first evaluated with another 20 traces and then applied to the entire dataset of 95 traces. During the training, we detected 573 process deviations in 24 activity traces that include 1099 activities. Among these deviations, only 27% represented true deviations and the remaining 73% were false alarms. This initial deviation detection accuracy was only 66.6%, with a F1-score of 0.42. Detection accuracy of the repaired system increased to 95.2% (0.85 F1-score) during system validation and to 98.5% (0.96 F1-score) during testing. After deploying the repaired deviation detection system to all 95 activity traces, we detected 1060 process deviations in 5659 activities (11.2 deviations per resuscitation). Among the 5659 activities in these traces, 4893 fit the repaired knowledge-driven workflow model, 294 were errors of omission, 538 were errors of commission, and 228 were scheduling errors. Our approach to automatic deviation detection provides a method for identifying repeated, omitted and out-of-sequence activities that can be included in the design of decision support systems for complex medical processes. Our findings show the importance of assessing detected deviations for repairing a knowledge-driven model that best represents "work as done."

Evolution of Cognitive Rehabilitation After Stroke From Traditional Techniques to Smart and Personalized Home-Based Information and Communication Technology Systems: Literature Review.

BackgroundNeurological patients after stroke usually present cognitive deficits that cause dependencies in their daily living. These deficits mainly affect the performance of some of their daily activities. For that reason, stroke patients need long-term processes for their cognitive rehabilitation. Considering that classical techniques are focused on acting as guides and are dependent on help from therapists, significant efforts are being made to improve current methodologies and to use eHealth and Web-based architectures to implement information and communication technology (ICT) systems that achieve reliable, personalized, and home-based platforms to increase efficiency and level of attractiveness for patients and carers.ObjectiveThe goal of this work was to provide an overview of the practices implemented for the assessment of stroke patients and cognitive rehabilitation. This study puts together traditional methods and the most recent personalized platforms based on ICT technologies and Internet of Things.MethodsA literature review has been distributed to a multidisciplinary team of researchers from engineering, psychology, and sport science fields. The systematic review has been focused on published scientific research, other European projects, and the most current innovative large-scale initiatives in the area. A total of 3469 results were retrieved from Web of Science, 284 studies from Journal of Medical Internet Research, and 15 European research projects from Community Research and Development Information Service from the last 15 years were reviewed for classification and selection regarding their relevance.ResultsA total of 7 relevant studies on the screening of stroke patients have been presented with 6 additional methods for the analysis of kinematics and 9 studies on the execution of goal-oriented activities. Meanwhile, the classical methods to provide cognitive rehabilitation have been classified in the 5 main techniques implemented. Finally, the review has been finalized with the selection of 8 different ICT–based approaches found in scientific-technical studies, 9 European projects funded by the European Commission that offer eHealth architectures, and other large-scale activities such as smart houses and the initiative City4Age.ConclusionsStroke is one of the main causes that most negatively affect countries in the socioeconomic aspect. The design of new ICT-based systems should provide 4 main features for an efficient and personalized cognitive rehabilitation: support in the execution of complex daily tasks, automatic error detection, home-based performance, and accessibility. Only 33% of the European projects presented fulfilled those requirements at the same time. For this reason, current and future large-scale initiatives focused on eHealth and smart environments should try to solve this situation by providing more complete and sophisticated platforms.

Recovering from Assembly Errors by Exploiting Human Demonstrations

A novel approach for recovering from errors during automatic assembly in typical mating operations is presented. It is based on automatic error detection w.r.t. a predefined process model, followed by choosing a recovery strategy from a repository. The latter comprises successful strategies that were recorded from human demonstration during a larger scale user study. The paper shows how to enhance the process model with additional data, how to record new strategies in case no suitable strategy is found, and how to select the most appropriate recovering strategy. The added value of the new approach is demonstrated by an experimental validation.

Формалізований метод проектування застосувань в технології GPGPU

The method of researching systems with high-performance computing support, based on the transition systems apparatus (discrete computational model), is proposed. Two variants of synchronous product limitations of transition systems that model tha Nvidia CUDA approach are proposed. transition systems that represent two types of instructions, process of the warp instruction execution, and the process of warp scheduling were described. GPGPU application execution model was formalized and its correctness was proved. Two variants of the relevant Petri net which allowed automatic or semi-automatic detection of design errors were obtained. Problems in programming 2018; 2-3: 012-020

Larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster

The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0

Method to Increase the Speed of EIS Series Measurements on Battery Stacks during Cycling While Avoiding Dramatic Loss of Measurement Accuracy

The amount of time expended while running experiments is a crucial factor to consider when screening new materials as candidates for improving battery performance. Electrochemical impedance spectroscopy (EIS) is among the most appropriate evaluation tools for collecting in-situ information about electrochemical processes occurring inside battery systems. However, during charging and discharging cycles, the electrodes, electrolytes, and related materials comprising battery systems are continuously undergoing changes in state over time, even at timescales shorter than the amount of time required to run a single charge-discharge experiment. For instance, voltage is changing during constant current load. Here, the application of EIS can become restricted because the prerequisite of steady state as a foundational principle to interpret the resulting EIS spectra is violated due to changes of state in between the time it takes to sweep through frequencies of interest. Trying to yield significant results accounting for drift effects requires implementing particular modeling measures. This abstract presents an ensemble of solutions to combat this issue. Parallel EIS, automatic drift correction of the measured time dependent signals, time interpolation by series measurements, and the application of the Z-HIT algorithm as post-processing of the data are the tools of choice. Simultaneous EIS is an effective time saving procedure. Otherwise, sequential EIS evaluation of n single cells in a stack increases time consumption by the factor n. Also, comparing the results for each individual cell to other cells is no longer directly comparable to an exact degree because the measurement drifts due to time effects are difficult to correct for. Second, precautions must be provided during the data acquisition phase in order to suppress the erroneous assignment of drift signals to the system response [1]. Furthermore, “non causal” spectra in the meaning of the Kramers-Kronig relations can be converted into correct spectra by interpolation of spectra series vs. time and, by removal of residual non-causalities with the help of the Z-HIT transform, which has the capability to reconstruct drift-affected data [2]. In many cases, the user would favor the comfortable possibility to significantly reduce the time for recording the EIS spectra by the application of multi-sine excitation. However, conventional multi-sine excitation normally is accompanied by significant losses of accuracy. An advanced technique provides the features of automatic drift correction and error detection known from single-sine excitation [3]. As a result, substantial loss in accuracy can be avoided. Experimental data obtained from a four-cell Li-FePO4 stack (13.2 V / 40 Ah) using this new multi-sine algorithm will be presented upon approval of this abstract for presentation. Keywords: Multi-sine, Battery stacks, EIS, True parallel measurements, Z-HIT

Anomaly-based annotation error detection in speech-synthesis corpora

We investigate the problem of automatic detection of annotation errors in single-speaker read-speech corpora used for speech synthesis. For the purpose of annotation error detection, we adopt an anomaly detection framework in which correctly annotated words are considered as normal examples on which the detection methods are trained. Misannotated words are then taken as anomalous examples which do not conform to normal patterns of the trained detection models. We propose and evaluate several anomaly detection models – Gaussian distribution based detectors, Grubbs’ test based detector, and one-class support vector machine based detector. Word-level feature sets including basic features derived from forced alignment and various acoustic, spectral, phonetic, and positional features are examined to find an optimal set of features for each anomaly detector. The results with F1 score being almost 89% show that anomaly detection could help detecting annotation errors in read-speech corpora for speech synthesis. Furthermore, dimensionality reduction techniques are also examined to automatically reduce the number of features used to describe the annotated words. We show that the automatically reduced feature sets achieve statistically similar results as the hand-crafted feature sets. We also conducted additional experiments to investigate both robustness of the proposed anomaly detection framework with respect to particular data sets used for development and evaluation and the influence of the number of examples needed for anomaly detection. We show that a reasonably good detection performance could be reached with using significantly fewer examples during the detector development phase. We also propose a concept of a voting detector – a combination of anomaly detectors in which each “single” detector “votes” on whether or not a testing word is annotated correctly, and the final decision is then made by aggregating the votes. Our results show that the voting detector has a potential to overcome each of the single anomaly detectors. Furthermore, we compare the proposed anomaly detection framework to a classification-based approach (which, unlike anomaly detection, needs to use anomalous examples during training) and we show that both approaches lead to statistically comparable results when all available anomalous examples are utilized during detector/classifier development. However, when a smaller number of anomalous examples are used, the proposed anomaly detection framework clearly outperforms the classification-based approach. A final listening test showed the effectiveness of the proposed anomaly-based annotation error detection for improving the quality of synthetic speech.

The Fractional Occupation Number Weighted Density as a Versatile Analysis Tool for Molecules with a Complicated Electronic Structure.

The fractional occupation number weighted density (FOD) analysis is explored as a general theoretical diagnostic for complicated electronic structures. Its main feature is to provide robustly and quickly the information on the localization of "hot" (strongly correlated and chemically active) electrons in a molecule. We demonstrate its usage in four different prototypical applications: 1) As a new and fast measure of the biradical character of polycyclic aromatic hydrocarbons, 2) for the selection of active orbital spaces in multiconfigurational or complete active space self consistent field (MCSCF/CASSCF) treatments, 3) as a possibility to describe molecular-energy landscapes consistently in regions with varying biradical character, as exemplified by partial double-bond torsions, and 4) as a powerful visualization method for static electron correlation effects in large biomolecules in connection with an efficient semi-empirical tight-binding molecular orbital scheme. The last application opens a full quantum-mechanical, unbiased route to the automatic detection of errors in experimental protein X-ray structures, such as false protonation states or misplaced atoms. In the first example, the complete (unfragmented) quantum-chemical calculation of the FOD for an entire metalloprotein with more than 7500 atoms is described.

Hawkeye: Cloud Computing Based Automated Video Error Detection in Real-Time

With the rapid development of video surveillance and broadcast systems monitoring the video quality becomes an important aspect to assure a better quality of service. Error detection is an important technique to measure the quality of videos transmitted over the unreliable network. With the advent of HDTV previously subtle errors in videos are becoming more prominent. In this paper, we want to propose a next generation cloud-based video error detection system using image/video processing technology for making the detection process real-time. At first, we introduce an automatic video error detection method and then propose a cloud computing platform. Finally, we integrate error detection method with cloud computing platform named Hawkeye to achieve a real-time video error detection system which ensures contents integrity and minimizes testing time and efforts required to keep ahead of other conventional quality check system. Extensive experiments on prominent datasets and telecasted videos show that the proposed algorithm is very much efficient to detect errors for video broadcast and surveillance applications in terms of computation time and the detection of distorted frames.

조음자질을 이용한 한국인 학습자의 영어 발화 자동 발음 평가

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

Automatic detection of MLC relative position errors for VMAT using the EPID-based picket fence test

Multi-leaf collimators (MLCs) ensure the accurate delivery of treatments requiring complex beam fluences like intensity modulated radiotherapy and volumetric modulated arc therapy. The purpose of this work is to automate the detection of MLC relative position errors ⩾0.5 mm using electronic portal imaging device-based picket fence tests and compare the results to the qualitative assessment currently in use. Picket fence tests with and without intentional MLC errors were measured weekly on three Varian linacs. The picket fence images analysed covered a time period ranging between 14–20 months depending on the linac. An algorithm was developed that calculated the MLC error for each leaf-pair present in the picket fence images. The baseline error distributions of each linac were characterised for an initial period of 6 months and compared with the intentional MLC errors using statistical metrics. The distributions of median and one-sample Kolmogorov–Smirnov test p-value exhibited no overlap between baseline and intentional errors and were used retrospectively to automatically detect MLC errors in routine clinical practice. Agreement was found between the MLC errors detected by the automatic method and the fault reports during clinical use, as well as interventions for MLC repair and calibration. In conclusion the method presented provides for full automation of MLC quality assurance, based on individual linac performance characteristics. The use of the automatic method has been shown to provide early warning for MLC errors that resulted in clinical downtime.

Quality Assurance and Support Measures for Solar Cooling on System Level

Within the IEA SHC Task 48 “Quality assurance and support measures for Solar Cooling” the analysis and evaluation of the systems has been one of the four main focuses. Here the activities are including the lab- and field-based characterization of the systems, the definition and application of performance figures, a guideline for a reliable monitoring procedure including methods for automated error detection and an updated overview on worldwide installed DEC systems including hints and good practice examples. They are furthermore leading to three different easy-to-use tools for solar cooling systems: The LCA tool considers environmental and energetic values for the evaluation, the PISTACHE tool offers a fast pre-sizing of the systems giving support for the planner in advance and finally an Excel tool allows a complete system evaluation using long term monitoring data.

Investigating Patterns of Writing Errors for Different L1 Groups through Error-Coded ESL Learners’ Essays

Automated error detection and feedback systems are becoming an important component of online writing practice services for ESL/EFL (English as a second/foreign language) learners. The main purposes of the study are to: (a) collect samples of essays written by ESL learners with different native language (or L1) backgrounds that are error-coded by an early version of an automated error-detection system (CritiqueTM) and trained human coders; and (b) identify some unique patterns of writing errors for different first language (L1) groups. Data analyzed in this study included 18, 439 TOEFL◯R CBT essays error-coded by CritiqueTM and a much smaller, combined sample of 480 TOEFL◯R CBT/TOEFL iBT◯R essays error-coded by trained human coders. A comparison of error rates across five different language groups showed some unique patterns: (a) the Arabic and Spanish groups were the highest on both spelling and punctuation errors; (b) the Korean and Japanese groups had the highest article error frequency; and (c) the Chinese group had the highest number of errors related to verb conjugations or adjective and noun inflections. The implications of these findings are discussed in terms of understanding the nature of L1-related writing errors and enhancing the automated error detection and feedback systems.

Multi-modal Learning-based Pre-operative Targeting in Deep Brain Stimulation Procedures.

Deep brain stimulation, as a primary surgical treatment for various neurological disorders, involves implanting electrodes to stimulate target nuclei within millimeter accuracy. Accurate pre-operative target selection is challenging due to the poor contrast in its surrounding region in MR images. In this paper, we present a learning-based method to automatically and rapidly localize the target using multi-modal images. A learning-based technique is applied first to spatially normalize the images in a common coordinate space. Given a point in this space, we extract a heterogeneous set of features that capture spatial and intensity contextual patterns at different scales in each image modality. Regression forests are used to learn a displacement vector of this point to the target. The target is predicted as a weighted aggregation of votes from various test samples, leading to a robust and accurate solution. We conduct five-fold cross validation using 100 subjects and compare our method to three indirect targeting methods, a state-of-the-art statistical atlas-based approach, and two variations of our method that use only a single modality image. With an overall error of 2.63±1.37mm, our method improves upon the single modality-based variations and statistically significantly outperforms the indirect targeting ones. Our technique matches state-of-the-art registration methods but operates on completely different principles. Both techniques can be used in tandem in processing pipelines operating on large databases or in the clinical flow for automated error detection.

Automated Error Detection for Developing Grammar Proficiency of ESL Learners

Thanks to natural language processing technologies, computer programs are actively being used not only for holistic scoring, but also for formative evaluation of writing. CyWrite is one such program that is under development. The program is built upon Second Language Acquisition theories and aims to assist ESL learners in higher education by providing them with effective formative feedback to facilitate autonomous learning and improvement of their writing skills. In this study, we focus on CyWrite’s capacity to detect grammatical errors in student writing. We specifically report on (1) computational and pedagogical approaches to the development of the tool in terms of students’ grammatical accuracy, and (2) the performance of our grammatical analyzer. We evaluated the performance of CyWrite on a corpus of essays written by ESL undergraduate students with regards to four types of grammatical errors: quantifiers, subject-verb agreement, articles, and run-on sentences. We compared CyWrite’s performance at detecting these errors to the performance of a well-known commercially available AWE tool, Criterion. Our findings demonstrated better performance metrics of our tool as compared to Criterion, and a deeper analysis of false positives and false negatives shed light on how CyWrite’s performance can be improved.

CAPTURING ENVIRONMENTAL SALIENCE VIA AUTOMATIC ERROR-DETECTION

Event Abstract Back to Event CAPTURING ENVIRONMENTAL SALIENCE VIA AUTOMATIC ERROR-DETECTION Malcolm Brunton1*, Steve Provost1 and Justin Gaetano1 1 Southern Cross University, Psychology, School of Health and Human Sciences, Australia Mismatch Negativity (MMN) is an event-related potential (ERP) to a deviant cue following an established sequence of sensory stimuli. Some accounts of MMN (e.g., predictive coding) propose that MMN is a unique new component reflecting the detection of change. In contrast, the adaptation hypothesis assumes MMN reflects the operation of ‘fresh’ neuronal pathways to overcome habituation of the standard. If correct, then the size of MMN will be critically dependent upon the rate at which habituation takes place to the standard stimulus. To date, almost all experiments concerned with MMN have employed simple synthetic sinusoidal tones, which could be expected to show similar habituation characteristics. Aims: To investigate the development of habituation of, and MMN generated by, stimuli which differ in the degree to which they are synthetic (e.g., sinusoidal tones) or natural (e.g., heartbeats). Method: Eight volunteers participated in an auditory MMN experiment using a series of roving ‘oddball’ trials involving two synthetic auditory cues (a 200 Hz and a 500 Hz tone) and two natural sounds (a heartbeat and a gunshot). Results: The ERPs to the different stimuli differed markedly, with a prominent P1 and P300a component being evident to the gunshot, and more characteristic N1 and N2 components being found to the tones. Strong habituation of the P300, N1 and N2 components was evident, but was considerably smaller to the P1. Conclusion: Differences in the initial response to the stimuli make it difficult to compare rates of habituation across the stimuli. However, habituation occurred to all stimuli. The direction of this change was in some cases consistent with an MMN response, but in others was in the opposite direction to that anticipated by a unique component account. These data thus fit more comfortably within an adaptation hypothesis account. Keywords: habituation, event-related potential, electroencephalograph, Adaptation hypothesis, Mismatch-negativity Conference: Southern Cross University 13th Annual Honours Psychology Research Conference, Coffs Harbour, New South Wales, Australia, 7 Oct - 7 Oct, 2016. Presentation Type: Research Topic: Psychology Citation: Brunton M, Provost S and Gaetano J (2016). CAPTURING ENVIRONMENTAL SALIENCE VIA AUTOMATIC ERROR-DETECTION. Front. Public Health. Conference Abstract: Southern Cross University 13th Annual Honours Psychology Research Conference. doi: 10.3389/conf.FPUBH.2016.02.00010 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Sep 2016; Published Online: 30 Sep 2016. * Correspondence: Mr. Malcolm Brunton, Southern Cross University, Psychology, School of Health and Human Sciences, Coffs Harbour, NSW, 2450, Australia, m.brunton.10@student.scu.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Malcolm Brunton Steve Provost Justin Gaetano Google Malcolm Brunton Steve Provost Justin Gaetano Google Scholar Malcolm Brunton Steve Provost Justin Gaetano PubMed Malcolm Brunton Steve Provost Justin Gaetano Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A study of binary local histogram correlation for automatic detection of optical printed Chinese character recognition errors

In optical printed Chinese character recognition (OPCCR), some recognition errors would often occur, especially in recognition of similar characters. The recognition errors are commonly checked and corrected manually, because automatic detection of the errors is difficult. Hence, in this paper, we propose a binary local histogram correlation (BLHC) method for automatic detection of OPCCR errors. In the method, the recognized characters are converted into OPCCR images. The images of optical printed Chinese character (OPCC) and OPCCR are filtered using differential low pass filter (DLPF) and segmented into binary images using adaptive threshold segmentation. The binary images are divided into blocks and the histograms of the blocks are selected as the features of local structures. The correlations of the local histograms between OPCC and OPCCR are utilized to determine whether OPCCR errors occur. In image block division, three division methods are studied to see the affects on error detection risk (EDR) and error detection rate. To validate the BLHC method, some experiments have been done. Experiment results showed that BLHC method using 1/2 offset second division has the advantages of simple programming, fast computation and high error detection rate.

An automatic 3D CAD model errors detection method of aircraft structural part for NC machining

Abstract Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach. Highlights The 3D CAD model of an aircraft structural part is partitioned into multiple local regions in terms of the base faces. The fillet association level graph is proposed to represent the local region for exploring the CAD model errors. An automatic CAD model error detection method based on heuristics rule is proposed.

Quantitative analysis of warnings in building information modeling (BIM)

Abstract Building information modeling (BIM) provides automatic detection of design-related errors by issuing warning messages for potential problems related to model elements. However, if not properly managed, the otherwise useful warning feature of BIM can significantly reduce the speed of model processing and increase the size of models. As the first study of its kind, this study proposes to apply the Pareto analysis to investigate BIM warnings in terms of type and frequency. Based on warning data collected from three California healthcare projects, the analysis revealed that the 15–80 rule applies across the case projects and their design phases—15% of the warning messages are responsible for nearly 80% of the warnings. Two other noteworthy findings include the following: (1) only the schematic design phase indicates a different Pareto rule of 25–80, as well as warning pattern from other design phases due to its unique purpose; and (2) the decisions of individual design teams are a major variable in the pattern of warning types. Lastly, time estimation for warning corrections is proposed based on learning curve theory to support efficient BIM warning management practices. The results and warning classifications presented in this study are expected to contribute to the design management and modeling practices of design teams involved in large, complex projects.

Instantaneous and automatic detection of auditory syntactic errors

Instantaneous and automatic detection of auditory syntactic errors