scholarly journals Machine learning - based framework for construction delay mitigation

2021 ◽  
Vol 26 ◽  
pp. 303-318
Author(s):  
Muizz O. Sanni-Anibire ◽  
Rosli M. Zin ◽  
Sunday O. Olatunji
Keyword(s):  
Machine Learning ◽  
Construction Industry ◽  
Cost Estimation ◽  
Construction Projects ◽  
Linear Regression Analysis ◽  
Ensemble Methods ◽  
Support Vector ◽  
Problem Solving Strategy ◽  
Duration Estimation ◽  
Solving Strategy

The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

scholarly journals Building Information Modelling Technology in Mitigating Cost Overruns and change orders in Construction Projects

2021 ◽  
Vol 4 (1) ◽  
pp. 50-64
Author(s):  
Ja’far A. Aldiabat Al-Btoosh
Keyword(s):  
Construction Industry ◽  
Cost Estimation ◽  
Construction Projects ◽  
Information Modeling ◽  
Cost Overruns ◽  
Change Orders ◽  
Significant Difference ◽  
Project Costs ◽  
Building Information ◽  
And Control

Variation order (VO) is one of the main issues faced by the construction industry in Jordan. Many researchers had investigated on the causes of VO and proposed procedures to minimize and control this issue; however, the VO is affecting the construction industry badly even at moment. Building Information Modeling (BIM) is a powerful management system that can make a significant difference in the project costs. However, BIM has not been examined as a tool to minimize the VO in Jordan. The main target of this study is to utilize BIM applications in reducing the effect of VO on the governmental projects in Jordan. In order to achieve this target, the researcher has designed a questionnaire to gather data related to VO causes and the BIM capability to solve this problem. The data collected from the questionnaires were analyzed statistically. The result from the analysis found that the consultant initiated the highest VO of 50% followed by the clients and the contractors of 20% and 10% unforeseen variation respectively. Moreover, it is found that BIM Design Applications, Facility Operations Simulation, Exploration Design Scenarios, BIM Design Detection and BIM Quantity Take-off and Cost Estimation were significantly capable of minimizing VO. The results show positive relationship with the application of BIM in minimizing VO in the construction industry in Jordan. © 2018. JASET, International Scholars and Researchers Association

scholarly journals Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

2021 ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Abdul Hannan Qureshi
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Industrial Revolution ◽  
Progress Monitoring ◽  
Concrete Block ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Material Classification ◽  
Artificial Neural Network Ann

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

scholarly journals Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

2021 ◽  
Vol 10 (19) ◽  
pp. 4576
Author(s):  
Dae Youp Shin ◽  
Bora Lee ◽  
Won Sang Yoo ◽  
Joo Won Park ◽  
Jung Keun Hyun
Keyword(s):  
Machine Learning ◽  
Clinical Chemistry ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Receiver Operator Characteristic Curve ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sensorimotor Polyneuropathy ◽  
Learning Techniques ◽  
Electrophysiological Findings

Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.

scholarly journals Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

2021 ◽  
Vol 73 (01) ◽  
pp. 1-13
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Prestressed Concrete ◽  
Cost Estimation ◽  
Gaussian Process Regression ◽  
Concrete Bridges ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Construction Costs ◽  
Prestressed Concrete Bridges

Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation.

scholarly journals Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

2020 ◽  
Vol 12 (8) ◽  
pp. 3269
Author(s):  
Shinyoung Kwag ◽  
Daegi Hahm ◽  
Minkyu Kim ◽  
Seunghyun Eem
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Seismic Performance ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Assessment Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

BUILDING INFORMATION MODELING (BIM) ROLES IN THE MALAYSIAN CONSTRUCTION INDUSTRY

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Aryani Ahmad Latiffi ◽  
Suzila Mohd ◽  
Juliana Brahim
Keyword(s):  
Construction Industry ◽  
Cost Estimation ◽  
Construction Projects ◽  
Building Information Modeling ◽  
Facility Management ◽  
Information Modeling ◽  
Structured Interviews ◽  
New Paradigm ◽  
Building Information ◽  
Potential Improvement

Building Information Modeling (BIM) represents a new paradigm in the Malaysian architecture, engineering, and construction (AEC) industry. BIM technology provides virtual models (including 3-D models) to generate a building’s entire lifecycle. The model can also be used for analyzing design clashes, project scheduling, cost estimation, and facility management. The use of BIM in construction projects can reduce time to develop a project, reduce construction cost, and increase project quality. This paper aims to explore roles of BIM in the Malaysian construction industry. Semi-structured interviews were conducted with project consultants and BIM consultants involved in two government projects. The projects were the National Cancer Institute (NCI) Malaysia and Sultan Ibrahim Hall (formerly known as the Multipurpose Hall of Universiti Tun Hussein Onn Malaysia, or UTHM). The interviews revealed effects of BIM in both projects and potential improvement in implementing BIM in construction projects in Malaysia. A literature review and the interviews revealed that BIM is increasingly used and accepted by construction players in Malaysia, and is expected to grow in future.

scholarly journals Determination of Direct to Indirect Accident Cost Ratio for Railway Construction Project

2019 ◽  
Vol 266 ◽  
pp. 03009 ◽  
Author(s):  
Nik Nur Khairunnisa Nik Mohd Ainul Azman ◽  
Asmalia Che Ahmad ◽  
Mohmad Mohd Derus ◽  
Izatul Farrita Mohd Kamar
Keyword(s):  
Construction Industry ◽  
Cost Estimation ◽  
Construction Projects ◽  
Light Rail ◽  
Cost Ratio ◽  
Permanent Disability ◽  
Railway Construction ◽  
Railway Construction Project ◽  
Self Administered Questionnaire

Construction industry involves dangerous activities which few are exposed to a high risk of being fatal, injuries and damages to machinery and property. The construction of Mass Rapid Transit (MRT) and Light Rail Transit (LRT) have no exception to those accidents. The accident can bring economic burden to project stakeholders especially contractors and client. However, the accident cost is relatively complicated because of its “hidden” or “invisible” portion. Thus, this paper is aimed to determine the ratio of direct to indirect accident cost for railway construction projects. The study was conducted using self-administered questionnaire distributed to safety practitioners (n=11) at MRT and LRT construction projects. A total of 36 out of 43 reportable accident cases successfully collected for the study and were analysed with simple descriptive statistics. The findings show that the accident cost ratio for fatality is 1:1.22, permanent disability is 1:1.94, and temporary disability is 1:1.19. The overall accident ratio for all accident classifications is 1:1.23. The findings of the current study may impact future safety cost estimation process in determining the hidden accident costs for railway construction projects.

scholarly journals On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach

Buildings ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 2 ◽  
Author(s):  
Michał Juszczyk
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Early Stage ◽  
Real Life ◽  
Percentage Error ◽  
Support Vector ◽  
Bridge Construction ◽  
Construction Costs ◽  
Cost Estimates ◽  
Comparable Performance

The completion of a bridge construction project within budget is one of the project’s key factors of success. This prerequisite is more likely to be achieved if the cost estimates, especially those provided in the early stage of a project, are realistic and close to the actual costs. The paper presents the research results on the development of a cost prediction model based on machine learning, namely the support vector machines (SVM) method, for which the input represents basic information and parameters of bridges, available in the early stage of projects. Several SVM-based regression models were investigated with the use of data collected for a number of bridge construction projects completed in Poland. Having finished the machine learning and testing processes, five of the models, of satisfying knowledge generalization ability and comparable performance, were preselected. The final selection of the best model was based on the comparison and analysis ability to predict bridge construction costs with accuracy appropriate for the early stage of projects. The general testing metrics of the finally selected model, named BCCPMSVR2, were as follows: root mean square error: 1.111; correlation coefficient of real-life bridge construction costs and costs predicted by the model: 0.980; and mean absolute percentage error: 10.94%. The research resulted in the development and introduction of an original model capable of providing early estimates of bridge construction costs with satisfactory accuracy.

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