scholarly journals Intelligent analysis in construction management

2018 ◽  
Vol 193 ◽  
pp. 05051
Author(s):  
Fjoder Klashanov
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Construction Industry ◽  
Construction Management ◽  
Model Building ◽  
Spoken Language ◽  
Management Process ◽  
Intelligent Analysis ◽  
Original Construction ◽  
Lexical Variable

The article is devoted to analysis of data mining techniques in a model of construction management. In the construction industry, as a rule, in most cases is incomplete and/or unclear information, so the classical methods of model-building control don't work. In this case, it is advisable to apply some well developed methods of fuzzy sets and fuzzy logic. The original construction is described by the words of the spoken language, where the sentences are predicates of the first kind. Identifies the parameters that influence the management process in the form of a lexical variable, which is analyzed by methods of the theory of many-valued logic. The results of the analysis are used in building the model administration of construction.

scholarly journals Application of BIM and 3D Laser Scanning for Quantity Management in Construction Projects

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Thu Anh Nguyen ◽  
Phong Thanh Nguyen ◽  
Sy Tien Do
Keyword(s):  
Construction Industry ◽  
Construction Projects ◽  
Laser Scanning ◽  
Essential Role ◽  
Industrial Revolution ◽  
Management Process ◽  
3D Laser Scanning ◽  
Economic Sectors ◽  
Construction Companies

The construction industry has played an essential role in the process of modernization and industrialization and it has also been a major factor in determining the development of the infrastructure for other economic sectors. Construction companies consider the measurement of work progress, which often wastes time and has a low resolution, to be one of the most challenging problems faced by project management. Therefore, this research aimed to propose practical solutions by applying recent technological achievements of the 4.0 industrial revolution to improve the efficiency of the quantity management process. By utilizing the advantages and features of a BIM model and 3D laser scanning, this paper proposes that adopting a BIM model and 3D laser scanning has the potential to improve the accuracy and efficiency of the quantity management process. The case study demonstrated some typical tasks to evaluate accuracy and efficiency as well as to showcase the research proposal.

scholarly journals Improving Construction Management Practice in the Gibraltar Construction Industry

2019 ◽  
Author(s):  
Emmanuel I. Daniel ◽  
Daniel Garcia ◽  
Ramesh Marasini ◽  
Shaba Kolo ◽  
Olalekan Oshodi
Keyword(s):  
Construction Industry ◽  
Construction Management ◽  
Management Practice

scholarly journals Characteristics of Procurement Methods in Malaysia – Comparing Design-Bid-Build, Design-Build and Construction Management

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Azeanita Suratkon ◽  
◽  
Riduan Yunus ◽  
Rafikullah Deraman ◽  
◽  
...  
Keyword(s):  
Construction Industry ◽  
Construction Management ◽  
Construction Projects ◽  
Building Construction ◽  
Technical Expertise ◽  
Time Cost ◽  
Project Complexity ◽  
Design Build ◽  
Procurement Method ◽  
Design Bid Build

Design-Bid-Build (DBB) or commonly known as Traditional method is the earliest and most prevalent procurement method used in Malaysian construction industry. Design-Build (DB) and Construction Management (CM) procurement methods were later introduced in Malaysia as an endeavour to satisfy and accommodate the increase in project complexity and the need for avoiding drawbacks of the Traditional methods. Each procurement method has different nature and possesses certain characteristics. Therefore, this study was carried out to ascertain and compare the characteristics of these three procurement methods that are implemented in building construction projects in Malaysia. A questionnaire survey was conducted among architects, consultants, contractors and owners or developers to elicit their feedback on the characteristics which were categorised into time, cost, quality, complexity and flexibility, degree of involvement and responsibility allocation and technical expertise. The findings indicated that only DB method almost fulfils all the characteristics under the six categories, whereas, DBB methods garnered agreement only for certain characteristics under time, cost, complexity and flexibility and technical expertise categories. Meanwhile, the only CM method’s characteristics that satisfy agreement from the respondents are the often used of fast track approach and lack of certainty in price. This study concludes that when a procurement method is adopted for a construction project, not all the features or characteristics will turn out as expected. There are many factors that contribute and are influential on the success in procurement methods that are worth for further investigation.

scholarly journals Applying Logistic Regression Data mining techniques for Ethiopian Government Agricultural Open Data Sets

2021 ◽  
Vol 10 (2) ◽  
pp. 473-488
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Agricultural Sector ◽  
Model Building ◽  
Open Data ◽  
Research Process ◽  
Attribute Selection ◽  
Sufficient Evidence ◽  
Vegetable Crops ◽  
Logistic Regression Algorithm

Ethiopia has a great agricultural potential because of its vast areas of fertile land, diverse climate, generally adequate rainfall, and large labor force. With its verified importance to the Ethiopian economy, there is sufficient evidence to show that the potential of the agricultural sector can be expanded considerably by attracting investors towards the sector. This study aims at applying classification techniques in developing a predictive model that can estimate yield production of vegetable crops and the correlation of crops based on their class. In the process of building a model, different steps were undertaken. Among the steps, data collection, data preprocessing and model building and validation were the major ones. Different tasks performed in each step are mentioned as follows. The data were collected Food and Agriculture Organization of the United Nations (FAO). Under preprocessing, data cleaning, discretization and attribute selection were done. The final step was model building and validation and it was performed using the selected tools and techniques. The data mining tool used in this research was Weka. In this software the logistic regression algorithm was selected since it is capable to score more accuracy. After successive experiments were done using this software, a model that can classify crop yield as high, medium and low with better accuracy to the extent of 88.6%. Experimental results show that logistic regression is a very helpful tool to depict the contribution of yield estimation and crop correlation. The reported findings are optimistic, making the proposed model a useful tool in the decision making process. Eventually, the whole research process can be a good input for further indepth research

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

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