scholarly journals Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning

2019 ◽  
Vol 9 (24) ◽  
pp. 5324 ◽  
Author(s):  
Will Y. Lin ◽  
Ying-Hua Huang
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Construction Projects ◽  
Early Stage ◽  
Predictive Performance ◽  
Information Modeling ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Rule Based ◽  
Building Information

Construction projects are usually designed by different professional teams, where design clashes may inevitably occur. With the clash detection tools provided by Building Information Modeling (BIM) software, these clashes can be discovered at an early stage. However, the number of clashes detected by BIM software is often huge. The literature states that the majority of those clashes are found to be irrelevant, i.e., harmless to the building and its construction. How to filter out these irrelevant clashes from the detection report is one of the issues to be resolved urgently in the construction industry. This study develops a method that automatically screens for irrelevant clashes by combining the two techniques of rule-based reasoning and supervised machine learning. First, we acquire experts’ knowledge through interviews to compile rules for the preliminary classification of clash types. Subsequently, the results of the initial classification inferred by the rules are added into the training dataset to improve the predictive performance of the classifiers implemented by supervised machine learning. The average predictive performance obtained by using the hybrid method is up to 0.96, which has been improved from the traditional machine learning process only using individual or ensemble learning classifiers by 6%–17%.

scholarly journals Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

2021 ◽  
Vol 14 (1) ◽  
pp. 288
Author(s):  
Mathieu Fokwa Soh ◽  
David Bigras ◽  
Daniel Barbeau ◽  
Sylvie Doré ◽  
Daniel Forgues
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Steel Structure ◽  
Information Modeling ◽  
Practical Case ◽  
Design Rules ◽  
Steel Construction ◽  
Building Information ◽  
The Cost

Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.

scholarly journals Time and Resource Management of Residential Apartment Construction using Building Information Modeling

2019 ◽  
Vol 8 (10) ◽  
pp. 4238-4246
Keyword(s):  
Resource Management ◽  
Cost Estimation ◽  
Construction Projects ◽  
Building Information Modeling ◽  
Early Stage ◽  
Building Design ◽  
Information Modeling ◽  
Cost Overruns ◽  
Building Information

To complete a project under the complicated situations, it is important to follow effective ways to use available tools and methods, taking into account present technology and management. Also, it is challenging to prepare accurate and achievable plans in large construction projects. Visualizing forms at an early stage in building design improves the ability of designers to deliver ideas and the capability to inspect and assess the methods helps to predict and optimize the actual presentation of the construction project. These different features form the basis for Building Information Modeling (BIM). The archetypal problems like budget overflow, lack of communication, cost overruns, overtime delays, rework can be minimized with the use of Building Information Modeling (BIM) tools for building design & resource management. In this paper, the concept of Building Information Modelling (BIM) is used for 3D modeling, which involves 4th dimension as Time (4D), 5th dimension of Cost (5D) of the project. A case study of G+5 residential apartment is presented for 3D BIM modeling and quantity take-off with the commercial software Autodesk Revit 2019. The 4D BIM carried out using Primavera P6 is explained with methodology for a case study. Further, Autodesk Navisworks Manage 2019 is used for 5D BIM, which includes a graphical presentation of the construction schedule and cost estimation of the case study.

Life Cycle of Construction Objects at Information Simulation of Buildings and Structures

2019 ◽  
pp. 65-72 ◽  
Keyword(s):  
Life Cycle ◽  
Construction Projects ◽  
Life Cycle Management ◽  
Information Modeling ◽  
Sources Of Information ◽  
Logical Scheme ◽  
Modeling Technology ◽  
Information Models ◽  
Building Information ◽  
The Russian Federation

The variants of the division of the life cycle of a construction object at the stages adopted in the territory of the Russian Federation, as well as in other countries are considered. Particular attention is paid to the exemplary work plan – "RIBA plan of work", used in England. A feature of this document is its applicability in the information modeling of construction projects (Building information Modeling – BIM). The article presents a structural and logical scheme of the life cycle of a building object and a list of works that are performed using information modeling technology at various stages of the life cycle of the building. The place of information models in the process of determining the service life of the building is shown. On the basis of the considered sources of information, promising directions for the development of the life cycle management system of the construction object (Life Cycle Management) and the development of the regulatory framework in order to improve the use of information modeling in construction are given.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 527
Author(s):  
Eran Elhaik ◽  
Dan Graur
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
A Priori ◽  
Neutral Theory ◽  
Dominant Mode ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Selective Sweeps ◽  
Two Factors ◽  
Negative Controls

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled “Soft sweeps are the dominant mode of adaptation in the human genome” (Schrider and Kern, Mol. Biol. Evolut. 2017, 34(8), 1863–1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut. 2018, 35(6), 1366–1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern’s paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

Prediction of CO2 Minimum Miscibility Pressure Using an Augmented Machine-Learning-Based Model

SPE Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Utkarsh Sinha ◽  
Birol Dindoruk ◽  
Mohamed Soliman
Keyword(s):  
Machine Learning ◽  
Phase Behavior ◽  
Hybrid Method ◽  
Gas Injection ◽  
Supervised Machine Learning ◽  
Design Parameters ◽  
Support Vector ◽  
Hydrocarbon Gases ◽  
Minimum Miscibility Pressure ◽  
Analytical Correlation

Summary Minimum miscibility pressure (MMP) is one of the key design parameters for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, the MMP value is used to tune compositional models along with proper fluid description constrained with other available basic phase behavior data, such as bubble point pressure and volumetric properties. In general, carbon dioxide (CO2) and hydrocarbon gases are the most common gases used for (or screened for) gas injection processes, and because of recent focus, they are used to screen for the coupling of CO2-sequestration and CO2-enhanced oil recovery (EOR) projects. Because the CO2/oil phase behavior is quite different than the hydrocarbon gas/oil phase behavior, researchers developed specialized correlations for CO2 or CO2-rich streams. Therefore, there is a need for a tool with expanded range capabilities for the estimation of MMP for CO2 gas streams. The only known and widely accepted measurement technique for MMP that is coherent with its formal definition is the use of a slimtube apparatus. However, the use of slimtube restricts the amount of data available, even though there are other alternative techniques presented over the last three decades, which all have various limitations (Dindoruk et al. 2021). Due to some of the complexities highlighted in Dindoruk et al. (2021) and time and resource requirements, there have been a number of correlations developed in the literature using mostly classical regression techniques with relatively sparse data using various combinations of limited input data (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Jaubert et al. 1998; Emera and Sarma 2005; Yuan et al. 2005; Ahmadi et al. 2010; Ahmadi and Johns 2011). In this paper, we present two separate approaches for the calculation of the MMP of an oil for CO2 injection: analytical correlation in which the correlation coefficients were tuned using linear support vector machines (SVMs) (Press et al. 2007; MathWorks 2020; RDocumentation 2020b; Cortes and Vapnik 1995) and using a hybrid method (i.e., superlearner model), which consists of the combination of random forest (RF) regression (Breiman 2001) and the proposed analytical correlation. Both models take the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and the reservoir temperature as input parameters. Based on statistical and data analysis techniques in combination with the help of corresponding crossplots, we showed that the performance of the final proposed method (hybrid method) is superior to all the leading correlations (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Emera and Sarma 2005; Yuan et al. 2005) and supervised machine-learning (Metcalfe 1982) methods considered in the literature (Altman 1992; Chambers and Hastie 1992; Chapelle and Vapnik 2000; Breiman 2001; Press et al. 2007; MathWorks 2020). The proposed model works for the widest spectrum of MMPs from 1,000 to 4,900 psia, which covers the entire range of oils within the scope of CO2 EOR based on the widely used screening criteria (Taber et al. 1997a, 1997b).

scholarly journals Challenges of the expansive use of Building Information Modeling (BIM) in construction projects

Production ◽  
2015 ◽  
Vol 25 (2) ◽  
pp. 289-297 ◽  
Author(s):  
Hannele Kerosuo ◽  
Reijo Miettinen ◽  
Sami Paavola ◽  
Tarja Mäki ◽  
Jenni Korpela
Keyword(s):  
Construction Projects ◽  
Building Information Modeling ◽  
Information Modeling ◽  
Building Information

scholarly journals Development of the structure and composition of a building information classifier towards the application of BIM technologies

Vestnik MGSU ◽  
2020 ◽  
pp. 867-906 ◽  
Author(s):  
Vladimir A. Volkodav ◽  
Ivan A. Volkodav
Keyword(s):  
Construction Industry ◽  
Construction Projects ◽  
Life Cycles ◽  
Classification Systems ◽  
Information Modeling ◽  
Information Classification ◽  
Technical Requirements ◽  
Information Models ◽  
Building Information ◽  
The One

Abstract Introduction. Various building information classification systems are used internationally; their critical analysis makes it possible to highlight basic requirements applicable to the Russian classifier and substantiate its structure and composition. Materials and methods. Modern international building information classification systems, such as OmniClass (USA), Uniclass 2015 (UK), CCS (Denmark), and CoClass (Sweden), are considered in the article. Their structure, composition, methodological fundamentals are analyzed. In addition to international classification systems, Russian construction information classifiers are analyzed. Results. The structure of a building information classifier has been developed and tailored to the needs of BIM (building information modeling) and national regulatory and technical requirements. The classifier’s structure complies with the one recommended by ISO 12006-2:2015. Its composition has regard to the requirements that apply to the aggregation and unification of Russian classifiers, and it also benefits from the classifiers developed for and used by the construction industry. The proposed building information classifier has four basic categories and 21 basic classes. Conclusions. The proposed structure and composition of a building information classifier represent a unified and universal tool for communicating building information or presenting it in the standardized format in the consolidated information space designated for information models needed to manage life cycles of major construction projects.

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