The high complexity of modern large‐scale construction projects leads their schedules to be sensitive to delays. At underground construction sites, the earthwork processes are vital, as most of the following tasks depend on them. This article presents a method for estimating the productivity of soil removal by combining two technologies based on computer vision: photogrammetry and video analysis. Photogrammetry is applied to create a time series of point clouds throughout excavation, which are used to measure the volume of the excavated soil for daily estimates of productivity. Video analysis is used to generate statistics regarding the construction activities for estimating productivity at finer time scales, when combined with the output from the photogrammetry pipeline. As there may be multiple causes for specific productivity levels, the automated generation of progress and activity statistics from both measurement methods supports interpretation of the productivity estimates. Comparison to annotated ground truth for the tracking and activity monitoring method highlights the reliability of the extracted information. The suitability of the approach is demonstrated by two case studies of real‐world urban excavation projects.
Inspection and maintenance of concrete bridges is a major cost factor in transportation infrastructure IntroductionDeterioration processes can reduce the reliability and the safety of engineering structures. Knowledge of the actual condition of the structure is therefore essential in order to comply with element and system target reliabilities (see [1][2][3]). Inspections and structural health monitoring are effective means of acquiring information on the actual condition of an ageing structure and should be utilized to reduce the uncertainty in engineering models and, ultimately, to optimize the management of deteriorating structures. This task is ideally carried out through a Bayesian analysis approach [4][5][6][7][8].As most deterioration processes are spatially distributed in structures and the deterioration progress at different locations in a structure is correlated, such an analysis should be performed considering the structure as a whole (e.g. [9][10][11][12]). However, the integral assessment of spatially distributed deterioration is computationally demanding, and novel computational strategies are required for probabilistic assessment and Bayesian updating of spatial deterioration [13,14]. Ultimately, the modelling approach and the computational methods should lead to software that can be used by engineers who are not experts in reliability analyses. Only in this way can Bayesian methods enhance the integrity management of ageing structures in practice.This paper describes an approach in which the uncertain system deterioration state of an ageing concrete structure is described by a Dynamic Bayesian Network (DBN) model of the underlying stochastic deterioration processes. This approach was motivated by the work of Straub [15] and Qin and Faber [16]. DBN models provide a computational framework that enables the evaluation of the joint distribution function of the system deterioration state based on the prior stochastic model and, in particular, Bayesian updating of the joint distribution function of the system deterioration state with observations of the underlying deterioration process, i.e. inspection and monitoring results.To prove the concept, the approach is implemented in a software prototype employing a DBN model of chloride-induced reinforcement corrosion, which can approximate the spatial correlation of the corrosion process. The prototype is applied to assess and update the deterioration state and structural reliability of a single-cell prestressed concrete box girder. This construction type is typical of long, multi-span highway bridges in Germany. However, the general solution strategy applies to all common concrete bridge structures.Section 2 gives an overview of the underlying system model. The deterioration model and the structural capacity model are described in more detail in sections 3 and 4. The implementation of the prototype is briefly described in section 5. A case study applying the prototype is summarized in section 6. System modelTo model the deterioration state, the box girder i...
Background More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID) DERR1-10.2196/35442
Background Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre‐symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR‐AD (http://www.radar-ad.org) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method An example of a panel of digital technology RADAR‐AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re‐ran VAMBN. Result Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub‐item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion Our results indicate that there is a significant dependency between digital readouts and clinical scores such as MMSE and FAQ. Therefore, DM’s may have the potential to act as a vital measure in the diagnosis of AD in a pre‐symptomatic stage. Our next steps will focus on evaluating the diagnostic benefit of DMs compared to the questionnaire‐based FAQ.
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