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The number of experts who realize the importance of big data continues to increase in various fields of the economy. Experts begin to use big data more frequently for the solution of their specific objectives. One of the probable big data tasks in the construction industry is the determination of the probability of contract execution at a stage of...
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... feature was taken into account while data processing. The extracted data from the "contract" and "contractProcedure" files were aggregated according to the contract number (Table 3). The data set on contracts is stored in the contracts.pickle ...
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... Chattapadhyay et al. [86] used a cross-analytical machine learning model with K-means clustering and Genetic Algorithm to exploit different risk factors and their impacts on the performance aspects of construction megaprojects. Valpeters et al. [87] determined the probability of contract execution risk at a given stage of its establishment using Logistic Regression, Decision Tree, and Random Forest algorithms. Creedy et al. benefited from Multivariate Regression Analysis for evaluating risk factors that lead to cost overruns in delivering highway construction projects. ...
... Probability-based reasoning Rule-based reasoning Fuzzy logic [44,50,87,94] Forward propagation and backpropagation Loss function Weights and biases [95,96] Structure ...
... Limited amount of data Able to deal with missing values Numerical, categorical, and linguistic data [103,104] High amount of data Partial ability to deal with missing values [24] Probability and dependencies' role Embrace probability in assessments Considering variables interdependencies with each other and final output [105,106] Does not embrace probability in assessments Considering variables interdependencies on final output [87,107] Prediction precision Mid-high [108] Very high [25] Application scope Subjective and uncertain problems with limited data [109] Objective and complex problems with abundant data [83] Application in RM processes ...
Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.
... The study adopted five Machine Learning (ML) algorithms to train models for forecasting the occurrence of accidents and their severity. Valpeters et al. (2018) employed ML approaches to the task of determination of successful contract execution in the construction organization. The results from the study suggested prognostic models based on ML algorithms. ...
The fragmented nature of construction industry coupled with its complex and dynamic nature demands for innovative technologies to record better performance in project execution. In this respect, Artificial Intelligence (AI) based techniques posit a viable means to attain requisite efficiency in performance and alleviate the productivity of construction organizations. The adoption of sustainable practices in Construction Supply Chains (CSCs) lowers the environmental impact, lowers the risk of failure, and boosts competitiveness. The present study attempts to unearth potential issues in the adoption of AI practices in CSCs. Initially, the study identifies potential issues in the implementation of AI-based frameworks in CSCs by performing an extensive literature review and brainstorming sessions with industry experts. The exercise results in identifying 17 critical issues confronting the adoption of AI in CSCs which were subsequently subjected to fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. The findings from the study reveal that “Lack of trust in AI outcomes”, “Exploitation by hackers, cybercrimes and privacy intrusion”, “Risk and cost associated with construction projects”, “Uncertain processing and functions of AI algorithms”, and “Unclear profits and advantages” were the top five influential causal issues that affect the adoption of AI in CSCs. This study is a novel attempt in the direction to identify and prioritize the potential issues in the adoption of AI-based frameworks in the Indian CSCs.
... The choice of a new area in construction and the project monitoring of smart buildings are tasks being gradually unlocked with the implementation of large data in the construction industry used in tackling and assessing the success factor of a contract at the pre-and post-contract stages (Valpeters, Kireev & Ivanov, 2018). D' Amico et al. (2018) stated that there is a need for adequate data that serves as the only barrier in the effective utilization of machine learning. ...
This book provides readers with an understanding of various concepts, benefits, and practices that the adoption of Fourth Industrial Revolution (4IR) technologies can bring when working towards sustainable construction goals.
As digitalization continues to advance rapidly, the pressures on stakeholders in the architecture, engineering, construction, and operation (AECO) industry to revamp and restructure their activities and outputs become increasingly prevalent. This research book explains the importance of various digital tools and principles to achieve sustainable construction projects. It adopts various standards and concepts to highlight how 4IR technologies could assist and accelerate construction sustainability. It is the first book to link construction management with various digital tools to enhance construction projects’ sustainability. The also provides an in-depth insight into the concept of sustainable construction 4.0 across both developing and developed countries for construction professionals, sustainability experts, researchers, educators, and other stakeholders.
... Given the advances in storage, in particular the possibility of Cloud storage [12], this is not a problem. The purpose of the part is to provide data for intelligent analysis -Data mining [13], Big Data [14][15][16][17][18][19][20], Deep Learning [21,22], etc. Intelligent data analysis and results are needed to shape the company's future strategy in this area. ...
Recently, the IT field has developed a number of promising solutions that can be used in construction: machine vision systems for quality building monitoring, new Wi-Fi standards with increased speed and information protection, improved types of fiber optic lines, new categories of twisted pairs, providing exceptionally high performance in bandwidth reliability. The list goes on. The introduction of new solutions does not negate the use of existing, time-tested ones. For these solutions, the implementation experience has been gained, they are often more economical than new ones, and the peculiarities of their operation have been studied. As a result, designing an automated system for construction is not a trivial task, as the total number of IT components (including new and existing) is very large. The paper proposes a design approach to address the problem. The approach is based on the use of elements of the set theory and the capabilities of database management systems. As a result, it is possible to improve the quality of the project and significantly shorten the design time. The approach is explained by the example of designing a structured cable system of a building.
... -system use of all components; First of all, we are talking about intelligent methods of data processing, in particular Big Data technology [4][5][6][7][8][9][10]. This technology allows you to quickly process a huge number of different types of data. ...
In many areas of the economy, including the construction industry, new IT technologies are being successfully introduced - Cloud computing, Big Data, Internet of Things etc. A significant place among these technologies is taken by the direction of “Machine Vision”. This technology allows to bring to a qualitatively different level a number of important applications in the construction industry and therefore deserves the close attention of specialists. For example, with the help of machine vision it is possible to consider and analyze the smallest defects invisible to the human eye (for example, when controlling the quality of an important element of construction structures - welds). The result is not affected by fatigue or inattention of the staff, you can work 24 hours, there are other advantages discussed in the article. The best effect is achieved with the complex application of new IT technologies. The most promising is the use of machine vision in combination with IoT and Big Data technologies. The paper explores the possibility of using machine vision in the construction industry as a stand-alone solution and in combination with other new IT technologies. The possibility of using unmanned aerial vehicles (drones) equipped with a machine vision system is being considered - for monitoring construction sites and surrounding areas.
... The paper explores issues related to the rational choice of the ADC and the corresponding interface for data processing systems in the construction industry and in the transportation sector. This topic has become especially relevant in connection with the advent of new technologies: Big Data [1][2][3][4][5][6][7][8][9] (allows you to process the signals of numerous sensors); Digital double [10] (allows improving the quality of construction due to the use in the digital model of information received from sensors installed on the building) and others. ...
In many applications in the construction industry and in the transportation sector, it is necessary to convert the original analog information into digital form and transmit this information for further computer processing using the appropriate interface. The question of the correct choice of conversion means (analog-to-digital converters, ADC) and transmission means (interfaces of peripheral devices) is very important because the characteristics of these devices largely determine the quality of the system. Meanwhile, in this area there is a fairly wide range of modern solutions, new trends are observed, which should be taken into account by system developers. Often systems are operated in conditions of high levels of industrial noise and interference. In a number of applications, it is required to process the signals of sensors (for example, building condition sensors) with very high accuracy with moderate processing speed requirements. The choice of a solution is significantly affected by the fact that modern building condition monitoring systems, automated control systems for technical processes in construction, and other systems are built on the basis of microcontrollers. In this study, the above issues are considered comprehensively, on the basis of this, possible solutions are proposed, practical recommendations are given.
... To process this information, you may need the whole arsenal of modern digital technologies: data storage, cloud computing, intelligent computing and others. The use of Big Data technology [7][8][9][10][11][12][13][14] seems particularly attractive, since it has accumulated a number of recent advances in the field of digital information processing. It should be noted that the feasibility of using a particular data processing technology depends on the scale of the problems to be solved. ...
Satellite technologies are successfully used in many areas of the economy, allowing you to solve problems that cannot be solved by other methods. The paper explores the possibility of using satellite technologies for monitoring buildings and structures located in remote areas where satellite technologies are the main type of communication (other types of communication are either impossible or economically inexpedient). It is shown that the most rational solution of the tasks set is the use of VSAT (Very Small Aperture Terminal) satellite technology. This technology allows you to deploy a satellite terminal in a short time, to establish communication and transfer data on the state of construction objects to a computer center located at a very large distance from the specified objects. A scheme for monitoring buildings is presented, including a geostationary artificial earth satellite, satellite terminals deployed at remote construction sites, the NOC (Communications control center, Network operations center) and the monitoring Center for all construction sites. The satellite is connected to subscriber terminals using a direct and reverse channel. Data from the NOC is transmitted to the monitoring Center via the Internet through a secure VPN connection. It is noted that the use of certain technical improvements in satellite technology directly depends on economic factors. It is indicated that the monitoring Center of all construction objects receives information on the state not only from remote, but also “ordinary” buildings and structures. It is noted that for efficient information processing it is necessary to use cloud computing, modern intellectual technologies, for large-scale tasks it is most promising to use Big Data technology.
... Therefore, variety of researches that fit in this category is very wide. (Valpeters, Kireev& Ivanov, 2018) research machine learning solution (specific type of big data application) to determine probability of successful contract execution in construction project. This type of solution could be adjusted in any other industry as well if historical data of contracts and actual outcomes is available. ...
... (Pradeepa & Manjula, 2016) describe solution of geographical data extraction from different web documents and media that can be visualized and further used in early stages of potential construction projects. (Taylan, Kabli, Porcel, & Herrera-Viedma, 2018) research similar challenge to (Valpeters et al., 2018) -how to build an automatic solution for the best contractor selection in each specific construction situation to have best possible outcome. ...
... All publications that are assigned to this category - Bilal et al. (2016b), Lede, Chunyan, and Meiying (2016), Omran, and Chen (2016), Pradeepa, and Manjula (2016), Taylan et al. (2018), Tian, Wang, Zhou, and Peng (2018), Liu (2016), Valpeters et al. (2018). ...
Technological advancement has led to tremendous increase of data. Many industries utilize big data to become more efficient or even to create new products or services. Applications of big data in construction industry has been extensively researched in Asia that can be explained with huge construction volumes in the area. This study is aimed at identifying big data applications in construction industry in time period beyond 2016.
Research object is construction industry, research subject is big data applications. Research methods used in this research are systematic literature overview and meta-analysis. Novelty of the research is classification of big data applications based on systematic literature overview.
Authors conclude that existing categorization (Bilal et al., 2016b) can be applied to researches about big data applications in construction industry published in 2016 and later. However, potential for new applications is identified in category of emerging trends triggered by big data and authors propose to perform cross-industry analysis to identify solutions that can be adopted to construction industry.
... It should be noted that of all the methods used in technology Data Mining and Big Data, cluster analysis is especially useful for the construction area. We can formulate the obvious conclusion: Big Data technology has very good prospects for the construction industry [1][2][3][4]. Accordingly, the role of the Data Warehouse in the field of construction has increased. It is possible to create the new forms of business, based on the use of technologies Data Warehouse and Big Data. ...
Today, in information technologies, the direction associated with the use of Data Warehouse (DW) is evolving very dynamically. Using DW, it is possible to implement two types of data analysis: OLAP-analysis: a set of technologies for the rapid processing of data presented as a multidimensional cube; Data Mining is an intelligent, deep analysis of data to detect previously unknown, practically useful patterns (in our case, the construction area). It is noted, that of all the methods used in technology Data Mining, cluster analysis is especially useful for the construction area. At present, the role of DW has increased, significantly due to the fact, that many methods and approaches of Data Mining have formed the basis of a new, promising method of Big Data. We will specify that, that Data processing from the Data Warehouse with the help of technology Big Data, allows to deduce researches in a building area to the higher level. The purpose of this work is to research of the possibilities of application of the Data Warehouse in the construction area. The article suggests the new approach to data analysis in the construction area, based on the use of Big Data technology and elements of OLAP - analysis. In the section “Discussion” is considering the possibility of the new promising business in the construction field, based on the application of Data Warehouse and technology Big Data.
