Application of KDD Techniques to Extract Useful Knowledge from Labor Resources Data in Industrial Construction Projects

Hammad, Ahmed; AbouRizk, Simaan; Mohamed, Yasser

doi:10.1061/(asce)me.1943-5479.0000280

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…Although the traditional way of making decisions in construction organisations is based on chief executives' experience and gut feelings, the fierce market conditions in construction today requires precision and process improvement to avoid any risks associated with financial and project management issues (Mehta, 2010). Being a relatively recent phenomenon for construction, data mining techniques were mostly introduced to the AEC sector at the beginning of the 2000s (Hammad et al, 2013). In the AEC sector, BI&A can be used in (i) analysing data from the generic, firm-wide level to the more specific, individual level, (ii) performing "what if" analyses for cash flow, revenue and profit,(iii) eliminating cumbersome Excel spread sheets, and (iv) integrating information from different financial software resources such as SAP or Tally for a complete view of the business (Mehta, 2010).…”

Section: Data Mining In the Aec Sectormentioning

confidence: 99%

Challenges and drivers for data mining in the AEC sector

Ahmed

Aziz

Tezel

et al. 2018

ECAM

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C h all e n g e s a n d d riv e r s fo r d a t a m i ni n g in t h e AEC s e c t o rAh m e d, V, Aziz, Z U H, Tez el, A a n d Ri az, Z h t t p:// dx. doi.o r g/ 1 0. 1 1 0 8/ E CAM-0 1-2 0 1 8-0 0 3 5 Ti t l e C h all e n g e s a n d d riv e r s fo r d a t a m i ni n g in t h e AEC s e c t o r A u t h o r s Ah m e d, V, Aziz, Z U H , Tez el, A a n d Ri az, Z Typ e Articl e U RL This ve r sio n is a v ail a bl e a t : h t t p:// u sir.s alfo r d. a c. u k/id/ e p ri n t/ 4 7 2 9 4/ P u b l i s h e d D a t e 2 0 1 8 U SIR is a di git al c oll e c tio n of t h e r e s e a r c h o u t p u t of t h e U niv e r si ty of S alfo r d. W h e r e c o py ri g h t p e r mi t s, full t e x t m a t e ri al h el d in t h e r e p o si to ry is m a d e fr e ely a v ail a bl e o nli n e a n d c a n b e r e a d , d o w nlo a d e d a n d c o pi e d fo r n o nc o m m e r ci al p riv a t e s t u dy o r r e s e a r c h p u r p o s e s . Pl e a s e c h e c k t h e m a n u s c ri p t fo r a n y fu r t h e r c o py ri g h t r e s t ri c tio n s. Fo r m o r e info r m a tio n, in cl u di n g o u r p olicy a n d s u b mi s sio n p r o c e d u r e , pl e a s e c o n t a c t t h e R e p o si to ry Te a m a t: u si r@ s alfo r d. a c. u k . Abstract: http://mc.manuscriptcentral.com/ecaam Engineering, Construction and Architectural Management Abstract Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector.Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings:The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus.

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Section: Data Mining In the Aec Sectormentioning

confidence: 99%

Challenges and drivers for data mining in the AEC sector

Ahmed

Aziz

Tezel

et al. 2018

ECAM

View full text Add to dashboard Cite

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“…labor resources, materials, equipment) are the main reasons for schedule delay and budget overrun in construction projects. erefore, relevant studies on labor resources arrangement attempted to introduce KDD techniques to discover valuable knowledge from large sets of labor resources data, thereby optimizing resource arrangement practices [53]. Meanwhile, knowledge-based simulation was employed to enhance the reliability of queuing systems during the course of construction projects [124].…”

Section: Bid/procurement Phasementioning

confidence: 99%

Research Status and Challenges of Data‐Driven Construction Project Management in the Big Data Context

Huang

Shi

Zuo

et al. 2021

Advances in Civil Engineering

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The construction industry is facing a data tsunami, while emerging information technologies (IT) show great potential for the effective processing of these data or information. However, a comprehensive review for technological change, the resulting process, and organizational changes in the Big Data context, especially from the angle of whole lifecycle of construction project, is lacking. To fill the void, related works published in the databases of Web of Science, Science Direct, and American Society of Civil Engineers library are systematically reviewed. The general trend in emerging IT application in terms of construction project management (CPM) phases, technology and application, and research topics are revealed. Following this analysis, the particularized proposals in relation to each of the main topics within CPM is discussed. Furthermore, according to the advances and limitations of the current literature, corresponding future agendas such as the implementation of comprehensive data-driven CPM scenario are proposed to bridge the gaps between theoretical research and practical demands.

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“…WEKA is an extensively used machine-learning tool in the construction industry for data mining, used for, among other things, clustering, classification, prediction and data prepossessing. Examples include as follows: a prediction model for construction litigation to predict the outcome of construction disputes (Pulket and Arditi, 2009); predicting construction cost over runs by using data-mining classification algorithms (Williams and Gong, 2014); a data-mining approach to extract the patterns for high-risk accidents in the construction industry (Amiri et al , 2015); occupational-health analyzes (Gerassis et al , 2017); a comparison of different highway alignments and identification of the alignment options (Kim et al , 2014); a means of selecting project-delivery methods using decision-support systems (Yoon et al , 2016); and knowledge discovery in data to analyze labor-resource management (Hammad et al , 2014).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Machine learning for the identification of competent project managers for construction projects in Nepal

Karki

Hadikusumo

2021

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Purpose Project manager’s competency is crucial in the construction sector for the successful completion of projects, particularly in the case of developing countries like Nepal. Therefore, it is very essential to select competent project managers by finding the competency factors required by them. Hence, this study aims to identify the characteristics of competent project managers by expert opinion method and to evaluate their competency level by a questionnaire survey to develop a prediction model using a supervised machine learning approach via Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool which predicts Project manager’s performance as “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal (from US$200,000 up to US$10M). Design/methodology/approach The data collection procedure for this research is based on an expert opinion method and survey. Expert opinion method is conducted to find the characteristics of a competent project manager by validating the top 15 competency factors based on literature review. The survey is conducted with the top management to assess their project manager’s competency level. Both qualitative and quantitative approaches are used to collect data for classification and prediction in WEKA, a machine learning tool. Findings The results illustrate that the project managers in Nepal have a high score in leadership skills, personal characteristics, team development and delegation, communication skills, technical skills, problem-solving/coping with situation skills and stakeholder/relationship management skills. Furthermore, among the seven classifiers (naïve Bayes, sequential minimal optimization [SMO], multilayer perceptron, logistic, KStar, J48 and random forest), the accuracy given by the SMO algorithm is highest of all in both the percentage split and k-folds cross validation method. The model developed using SMO classifier by k-folds cross-validation (k = 10) is acknowledged as a final model. Research limitations/implications This research focuses to develop a prediction model to predict and analyze the competency of project managers by applying a supervised machine learning approach. Seven extensively used algorithms (Naïve Bayes, SMO, multilayer perceptron, logistic, KStar, J48, random forest) are used to check the accuracy of models and an algorithm that gives the highest accuracy is adopted. Data collection for this research is carried out by expert opinion method to validate the characteristics (factors) essential for competent project managers in the first round and the description of each factor as high, medium and low is inquired with the same experts in the second round. After an expert opinion, a structured questionnaire is prepared for the survey to assess the competency level of project managers (PMs). The competency level of PMs working under government funded, foreign aided or private projects from the contractor’s side is measured. This research is limited to the medium scale construction projects of Nepal. Practical implications This model can be a huge asset in the human resource department of construction companies as it helps to know the performance level of project managers in terms of “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal. Also, the model will assist human intelligence to make the decision while recruiting a new project manager/s for different types of projects at a time. Moreover, the model can be used for self-assessment of project manager/s to know their performance level. The model can be used to develop a user friendly interface system or an application such that it can be conveniently used anywhere any time. Social implications This research shows that most of the project managers working in a medium complexity construction project of Nepal are male, maximum of them hold bachelor’s degree and study for road projects. Furthermore, most of the project managers scored high in leadership skills, personal characteristics, communication skills, technical skills, problem-solving/coping with situation skills, team development and delegation and stakeholder/relationship management skills. The model has given the “Personal characteristics” attribute the highest weightage. Likewise, other attributes having high weightage are communication skills, analytical abilities, project budget, stakeholder/relationship management, team development and delegation and time management skills. Originality/value This research was conducted to find the competency factors and to study the competency level of project managers in Nepal to develop a prediction model to predict the PM’s performance using a machine learning approach in medium scale construction projects. There is a lack of research to develop a model that predicts project manager’s competency using the machine learning approach. Therefore, the predictive model developed here helps in the identification of a competent project manager as it will be advantageous for project completion with a high success rate.

show abstract

Application of KDD Techniques to Extract Useful Knowledge from Labor Resources Data in Industrial Construction Projects

Cited by 23 publications

References 9 publications

Challenges and drivers for data mining in the AEC sector

Challenges and drivers for data mining in the AEC sector

Research Status and Challenges of Data‐Driven Construction Project Management in the Big Data Context

Machine learning for the identification of competent project managers for construction projects in Nepal

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