PREDICTING PRODUCTIVITY LOSS CAUSED BY CHANGE ORDERS USING THE EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL

Change orders in construction projects are very common and result in negative impacts on various project facets. The impact of change orders on labor productivity is particularly difficult to quantify. Traditional approaches are inadequate to calculate the complex input-output relationship necessary to measure the effect of change orders. This study develops the Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM) to more accurately predict change-order-related productivity losses. The EFSIM is an AI-based tool that combines fuzzy logic (FL), support vector machine (SVM), and fast messy genetic algorithm (fmGA). The SVM is utilized as a supervised learning technique to solve classification and regression problems; the FL is used to quantify vagueness and uncertainty; and the fmGA is applied to optimize model parameters. A case study is presented to demonstrate and validate EFSIM performance. Simulation results and our validation against previous studies demonstrate that the EFSIM predicts the impact of change orders significantly better than other AI-based tools including the artificial neural network (ANN), support vector machine (SVM), and evolutionary support vector machine inference model (ESIM).

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A dynamic Modelling of Labor Productivity Impacts Arising from Change Orders in Road Projects

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0456 ◽

2021 ◽

Author(s):

Zain Ghazi Al-Kofahi ◽

Amirsaman Mahdavian ◽

Amr Oloufa

Keyword(s):

Labor Productivity ◽

Construction Projects ◽

Productivity Loss ◽

Dynamic Modelling ◽

Change Orders ◽

Factors Affecting ◽

Road Projects ◽

Change Order ◽

The Impact ◽

Sd Model

It is vital to investigate the system dynamics (SD) between the change orders and labor productivity to identify the causes of the productivity loss of the construction projects. Most productivity loss studies were financed from the contractor’s part and rely on the contractor’s data. This research highlighted the problem of productivity loss resulting from issuance of a change order by utilizing a previously developed SD model. It conducted a sensitivity analysis to evaluate the impact of overtime, overmanning, temperature and learning on the behavior of the SD model quantifying change orders' impact on labor productivity. Based on the results, SD provides more reliable results comparing with the measured mile analysis (MMA) approach for the compensation request, considering the leading factors affecting the productivity loss other than the change order. The model developed in this study can accept or reject the responsibility of a change order for occurrence of productivity loss.

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Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Expert Systems with Applications ◽

10.1016/j.eswa.2011.08.081 ◽

2012 ◽

Vol 39 (2) ◽

pp. 1737-1746 ◽

Cited By ~ 15

Author(s):

Min-Yuan Cheng ◽

Andreas F.V. Roy ◽

Kuan-Lin Chen

Keyword(s):

Support Vector Machine ◽

Risk Preference ◽

Support Vector ◽

Inference Model ◽

Fuzzy Support Vector Machine ◽

Collapse Prediction ◽

Slope Collapse ◽

Road Slope

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TIME DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL FOR PREDICTING DIAPHRAGM WALL DEFLECTION

Neural Network World ◽

10.14311/nnw.2014.24.012 ◽

2014 ◽

Vol 24 (2) ◽

pp. 193-210

Author(s):

Andreas F. V. Roy ◽

Min-Yuan Cheng ◽

Yu-Wei Wu

Keyword(s):

Support Vector Machine ◽

Time Dependent ◽

Diaphragm Wall ◽

Support Vector ◽

Inference Model ◽

Fuzzy Support Vector Machine ◽

Wall Deflection

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Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model

Automation in Construction ◽

10.1016/j.autcon.2010.02.008 ◽

2010 ◽

Vol 19 (5) ◽

pp. 619-629 ◽

Cited By ~ 44

Author(s):

Min-Yuan Cheng ◽

Hsien-Sheng Peng ◽

Yu-Wei Wu ◽

Te-Lin Chen

Keyword(s):

Support Vector Machine ◽

Construction Projects ◽

Support Vector ◽

Inference Model

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Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

International Journal of Geotechnical Earthquake Engineering ◽

10.4018/jgee.2011070103 ◽

2011 ◽

Vol 2 (2) ◽

pp. 29-39 ◽

Cited By ~ 1

Author(s):

Sarat Kumar Das ◽

Pijush Samui ◽

Dookie Kim ◽

N. Sivakugan ◽

Rajanikanta Biswal

Keyword(s):

Support Vector Machine ◽

Model Equation ◽

Lateral Displacement ◽

Least Square ◽

Disaster Mitigation ◽

Support Vector ◽

Model Parameters ◽

Ann Models ◽

Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

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Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

HighTech and Innovation Journal ◽

10.28991/hij-2021-02-04-02 ◽

2021 ◽

Vol 2 (4) ◽

pp. 285-292

Author(s):

Sugiyarto Surono ◽

Tia Nursofiyani ◽

Annisa E. Haryati

Keyword(s):

Support Vector Machine ◽

Performance Evaluation ◽

Membership Function ◽

Evaluation Model ◽

Distance Matrix ◽

Support Vector ◽

Complex Data ◽

Fuzzy Support Vector Machine ◽

Optimization Function ◽

The Impact

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PDF

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Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

International Journal of Computational Physics Series ◽

10.29167/a1i1p120-130 ◽

2018 ◽

Vol 1 (1) ◽

pp. 120-130 ◽

Cited By ~ 1

Author(s):

Chunxiang Qian ◽

Wence Kang ◽

Hao Ling ◽

Hua Dong ◽

Chengyao Liang ◽

...

Keyword(s):

Support Vector Machine ◽

Environmental Factors ◽

Cross Validation ◽

Concrete Strength ◽

Simulation Method ◽

Support Vector ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Influence Degree ◽

Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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