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Burke, R D, Parrish, K and El Asmar, M (2018) Environmental Product Declarations: Use in the Architectural and Engineering Design Process to Support Sustainable Construction. Journal of Construction Engineering and Management, 144(05).
Delgado, L, Shealy, T, Garvin, M and Pearce, A (2018) Framing Energy Efficiency with Payback Period: Empirical Study to Increase Energy Consideration during Facility Procurement Processes. Journal of Construction Engineering and Management, 144(05).
Doloi, H (2018) Community-Centric Model for Evaluating Social Value in Projects. Journal of Construction Engineering and Management, 144(05).
El-Sabek, L M and McCabe, B Y (2018) Framework for Managing Integration Challenges of Last Planner System in IMPs. Journal of Construction Engineering and Management, 144(05).
Farshchian, M M and Heravi, G (2018) Probabilistic Assessment of Cost, Time, and Revenue in a Portfolio of Projects Using Stochastic Agent-Based Simulation. Journal of Construction Engineering and Management, 144(05).
Liang, Q, Leung, M and Cooper, C (2018) Focus Group Study to Explore Critical Factors for Managing Stress of Construction Workers. Journal of Construction Engineering and Management, 144(05).
Lines, B C and Ravi Kumar, G G (2018) Developing More Competitive Proposals: Relationship between Contractor Qualifications-Based Proposal Content and Owner Evaluation Scores. Journal of Construction Engineering and Management, 144(05).
Mahpour, A and Mortaheb, M M (2018) Financial-Based Incentive Plan to Reduce Construction Waste. Journal of Construction Engineering and Management, 144(05).
Nguyen, D A, Garvin, M J and Gonzalez, E E (2018) Risk Allocation in U.S. Public-Private Partnership Highway Project Contracts. Journal of Construction Engineering and Management, 144(05).
Rocha, C G d, Anzanello, M J and Gerchman, M (2018) Method to Assess the Match between Clients’ Input and Decoupling Points in Customized Building Projects. Journal of Construction Engineering and Management, 144(05).
Schuldt, S and El-Rayes, K (2018) Optimizing the Planning of Remote Construction Sites to Minimize Facility Destruction from Explosive Attacks. Journal of Construction Engineering and Management, 144(05).
Tembo-Silungwe, C K and Khatleli, N (2018) Identification of Enablers and Constraints of Risk Allocation Using Structuration Theory in the Construction Industry. Journal of Construction Engineering and Management, 144(05).
Yap, J B H, Abdul-Rahman, H and Wang, C (2018) Preventive Mitigation of Overruns with Project Communication Management and Continuous Learning: PLS-SEM Approach. Journal of Construction Engineering and Management, 144(05).
Zhang, M, Cheng, W and Wang, Y (2018) Multiple-Fault Classification for Hot-Mix Asphalt Production by Machine Learning. Journal of Construction Engineering and Management, 144(05).
- Type: Journal Article
- Keywords: Hot-mix asphalt; Multiple-fault classification; Support vector machines; Statistical and shape features;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001470
- Abstract:
To monitor the condition of the hot-mix asphalt production process, the quality and consistency of input aggregates are widely used for monitoring process variation. Current practice involves taking samples from actual production and subsequently analyzing them in the laboratory. The entire process can take up to 2 h, which, along with being expensive, is not amenable to find a single-fault pattern or multiple-fault patterns. In this paper, an intelligent hybrid classifier is proposed that can be used to recognize the hot-mix asphalt multiple-fault patterns with satisfactory accuracy. Statistical and shape features are extracted from the observation data, and the principal component analysis (PCA) is further applied to the statistical and shape features to extract effective features for the classifier. Multi-class support vector machines (MSVMs) with an adaptive mutation particle swarm optimization (AMPSO) are applied to recognize the multiple-fault patterns automatically. Simulation results show that this approach can effectively recognize multiple-fault patterns for a hot-mix asphalt production process. The proposed model has potentially good application in hot-mix asphalt fault diagnosis. The specific findings can be described in three aspects. First, by comparing to the method of using preselected parameters and the cross-validation method, the authors identified that the proposed AMPSO algorithm provides a better combination of parameters for the MSVM classifier so that the recognition rate of fault patterns is much improved. Second, the studies show that the extracted features by the PCA applied to the statistical and shape features can significantly improve the recognition accuracy. Third, this study also shows that the proposed method can deliver satisfying prediction results even with relatively small-sized training samples.