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  • 1
    Online Resource
    Online Resource
    Condition Prediction for Existing Educational Facilities Using Artificial Neural Networks and Regression Analysis
    MDPI AG ; 2022
    In:  Buildings Vol. 12, No. 10 ( 2022-09-23), p. 1520-
    Details
    In: Buildings, MDPI AG, Vol. 12, No. 10 ( 2022-09-23), p. 1520-
    Abstract: Infrastructural assets such as roads, bridges, and buildings make a considerable contribution to national economies. These assets deteriorate due to aging, environmental conditions, and other external factors. Maintaining the performance of an asset in line with rational repair strategies represents a considerable challenge for decision-makers, who may not pay attention to developing adequate maintenance plans or leave the assets unmaintained. Worldwide, organizations are under pressure to ensure the sustainability of their assets. Such organizations may burden their treasury with random maintenance operations, especially with a limited budget. This research aims to develop a generalized condition assessment approach to monitor and evaluate existing facility elements. The proposed approach represents a methodology to determine the element condition index (CI). The methodology is reinforced with an artificial neural network (ANN) model to predict the element deterioration. The performance of this model was evaluated by comparing the obtained predicted CIs with ordinary least squares (OLS) regression model results to choose the most accurate prediction technique. A case study was applied to a group of wooden doors. The ANN model showed reliable results with R2 values of 0.99, 0.98, and 0.99 for training, cross-validation, and testing sets, respectively. In contrast, the OLS model R2 value was 1.00. These results show the high prediction capability of both models with an advantage to the OLS model. Applying this approach to different elements can help decision-makers develop a preventive maintenance schedule and provide the necessary funds.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    URL: Article
    DOI: 10.3390/buildings12101520
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661539-3
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  • 2
    Online Resource
    Online Resource
    Financial Contract Administration in Construction via Cryptocurrency Blockchain and Smart Contract: A Proof of Concept
    MDPI AG ; 2022
    In:  Buildings Vol. 12, No. 8 ( 2022-07-22), p. 1072-
    Details
    In: Buildings, MDPI AG, Vol. 12, No. 8 ( 2022-07-22), p. 1072-
    Abstract: The blockchain that uses cryptocurrency is a paradigm shift in the way of data storage, retrieval, and verification due to the concept of decentralization. This paradigm is essential to ensure the security of crucial data in any project. Adding a smart contract to the blockchain would facilitate the automation of various processes. Thus, the cryptocurrency blockchain that uses the smart contract can be considered a suitable platform for an ecosystem of many industries. The construction industry needs a highly secure automated management system due to its complex contractual relationships and transactions between parties. Therefore, integrating the blockchain with the smart contract creates the most appropriate ecosystem to be developed. This study introduces an ecosystemic prototype using a programmable smart contract within a novel cryptocurrency blockchain for construction. The purpose of the prototype is to guarantee a decentralized system as an independent economic environment for the construction industry. The system guarantees the security of financial transactions and focuses on the payment clauses in the construction contract as well. The results depended on three well-known hypothetical case scenarios from the construction site and were displayed in the form of extracted access data tables. The prototype proved the efficiency of the decentralized system for the construction industry by minimizing human-factor interference in the transaction process and thus reducing time waste and cost.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    URL: Article
    DOI: 10.3390/buildings12081072
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661539-3
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  • 3
    Online Resource
    Online Resource
    Neurogenic Appendicitis: A Reappraisal of the Clinicopathological Features and Pathogenesis
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 6 ( 2022-06-03), p. 1386-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 6 ( 2022-06-03), p. 1386-
    Abstract: In 1921; Masson and Maresch first coined the term “neurogenic appendicitis (NA)” to describe “neuroma-like” lesions in the appendix. To date, our knowledge about NA is limited; therefore, we conducted a comprehensive analysis of the literature (1921 to 2020) to examine the clinicopathological features of NA. We also addressed the pathophysiology of acute abdominal pain and fibrosis in this entity. We performed a meta-analysis study by searching the PubMed database, using several keywords, such as: “appendix,” “neurogenic,” “obliterative,” “neuroma,” “fibrous obliteration,” “appendicopathy,” and “appendicitis.” Our study revealed that patients with NA usually present clinically with features of acute appendicitis, bud2t they have grossly unremarkable appendices. Histologically, the central appendiceal neuroma was the most common histological variant of NA, followed by the submucosal and intramucosal variants. To conclude, NA represents a form of neuroinflammation. The possibility of NA should be considered in patients with clinical features of acute appendicitis who intraoperatively show a grossly unremarkable appendix. Neuroinflammation and neuropeptides play roles in the development of pain and fibrosis in NA.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12061386
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 4
    Online Resource
    Online Resource
    Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains
    MDPI AG ; 2023
    In:  Water Vol. 15, No. 19 ( 2023-10-09), p. 3525-
    Details
    In: Water, MDPI AG, Vol. 15, No. 19 ( 2023-10-09), p. 3525-
    Abstract: In surge protection, low-head profiles are deemed a challenge in pump failure events since they are prone to severe negative pressure surges that require an uneconomical surge vessel volume. A hybrid surge vessel with a dipping tube can provide required protection with reasonable economic volume. This work presents novel analyses for the hybrid surge vessel and develops a simple model for its optimum sizing using a stochastic numerical approach coupled with machine learning. Practical ranges for correct sizing of vessel components, such as ventilation tube, inlet/outlet air valves, and compression chamber, are presented for optimal protection and performance. The water hammer equations are iteratively solved using the hybrid surge vessel’s revised boundary conditions within the method of characteristics numerical framework to generate 2000 cases representing real pump failures on low-head pipelines. Genetic programming is utilized to develop simple relations for prediction of the hybrid vessel initial and expanded air volumes in addition to the compression chamber volume. Moreover, the developed model presented a classification index for low-head pipelines on which the hybrid vessel would be most economical. The developed model yielded good prediction error statistics. The developed model proves to be more accurate and easier to use than the classical design charts for the low-head pumping mains. The model clearly showed the relation between various hydraulic and pipe parameters, with pipe diameter and static head as the most influencing parameters on compression chamber volume and expanded air volume. The developed model, together with the classification indices, can be used for preliminary surge protection sizing for low-head pipelines.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w15193525
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2521238-2
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