An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations

Document identifier: oai:DiVA.org:ltu-75970
Access full text here:10.1080/00207233.2019.1662186
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Blasting, Ground vibration, PPV, ANN, MLR, Mining and Rock Engineering, Gruv- och berganläggningsteknik
Publication year: 2020
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Abstract:

This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted, and the Levenberg–Marquardt algorithm was used in training the network. The powder factor, the maximum charge per delay, and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets, as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R2) of the proposed ANN-based model was the highest.

Authors

Abiodun Ismail Lawal

Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Other publications >>

Musa Adebayo Idris

Luleå tekniska universitet; Geoteknologi; Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Other publications >>

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    header: 
        identifier: oai:DiVA.org:ltu-75970
        datestamp: 2021-04-19T12:42:28Z
        setSpec: SwePub-ltu
    metadata: 
        mods: 
            @attributes: 
                version: 3.7
            recordInfo: 
                recordContentSource: ltu
                recordCreationDate: 2019-09-12
            identifier: 
                http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75970
                10.1080/00207233.2019.1662186
                2-s2.0-85071993178
            titleInfo: 
                @attributes: 
                    lang: eng
                title: An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations
            abstract: 
This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted and the Levenberg–Marquardt algorithm was used in training the network. The powder factor the maximum charge per delay and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R2) of the proposed ANN-based model was the highest.

            subject: 
                    @attributes: 
                        lang: eng
                        authority: uka.se
                    topic: 
                        Engineering and Technology
                        Civil Engineering
                        Other Civil Engineering
                    @attributes: 
                        lang: swe
                        authority: uka.se
                    topic: 
                        Teknik och teknologier
                        Samhällsbyggnadsteknik
                        Annan samhällsbyggnadsteknik
                    @attributes: 
                        lang: eng
                    topic: Blasting
                    @attributes: 
                        lang: eng
                    topic: ground vibration
                    @attributes: 
                        lang: eng
                    topic: PPV
                    @attributes: 
                        lang: eng
                    topic: ANN
                    @attributes: 
                        lang: eng
                    topic: MLR
                    @attributes: 
                        lang: eng
                        authority: ltu
                    topic: Mining and Rock Engineering
                    genre: Research subject
                    @attributes: 
                        lang: swe
                        authority: ltu
                    topic: Gruv- och berganläggningsteknik
                    genre: Research subject
            language: 
                languageTerm: eng
            genre: 
                publication/journal-article
                ref
            note: 
                Published
                2
                
Validerad;2020;Nivå 1;2020-04-22 (alebob)

            name: 
                    @attributes: 
                        type: personal
                    namePart: 
                        Lawal
                        Abiodun Ismail
                    role: 
                        roleTerm: aut
                    affiliation: Department of Mining Engineering Federal University of Technology Akure Nigeria
                    @attributes: 
                        type: personal
                        authority: ltu
                    namePart: 
                        Idris
                        Musa Adebayo
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Luleå tekniska universitet
                        Geoteknologi
                        Department of Mining Engineering Federal University of Technology Akure Nigeria
                    nameIdentifier: 
                        idrmus
                        0000-0002-3838-8472
            originInfo: 
                dateIssued: 2020
                publisher: Taylor & Francis
            relatedItem: 
                @attributes: 
                    type: host
                titleInfo: 
                    title: International Journal of Environmental Studies
                identifier: 
                    0020-7233
                    1029-0400
                part: 
                    detail: 
                            @attributes: 
                                type: volume
                            number: 77
                            @attributes: 
                                type: issue
                            number: 2
                    extent: 
                        start: 318
                        end: 334
            physicalDescription: 
                form: print
            typeOfResource: text