A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground
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A Self‑adaptive differential evolutionary extreme learning machine (SaDE‑ELM): a novel approach to blast‑induced ground vibration prediction Clement Kweku Arthur1 · Victor Amoako Temeng1 · Yao Yevenyo Ziggah2 Received: 13 June 2020 / Accepted: 29 September 2020 © Springer Nature Switzerland AG 2020
Abstract Blast-induced ground vibration is still an adverse impact of blasting in civil and mining engineering projects that need much consideration and attention. This study proposes the use of Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) for the prediction of ground vibration due to blasting using 210 blasting data points from an open pit mine in Ghana. To ascertain the predictive performance of the proposed SaDE-ELM approach, several artificial intelligence and empirical approaches were developed for comparative purposes. The performances of various developed models were assessed using model performance indicators of mean squared error (MSE), Nash–Sutcliffe Efficiency Index (NSEI) and correlation coefficient (R). Furthermore, the Bayesian Information Criterion (BIC) was applied to select the best performing approach. The obtained prediction results based on the performance indicators showed that the SaDE-ELM outperformed all the competing models as it had the lowest MSE value of 0.01942, respectively. The SaDE-ELM also achieved the highest R and NSEI values of 0.8711 and 0.7537, respectively. The other artificial intelligent approaches had MSE, R and NSEI in the ranges of (0.02166–0.03006), (0.8012–0.8537) and (0.6188–0.7254), respectively. The empirical approaches performed poorly relative to the artificial intelligence approaches by having had MSE, R and NSEI in the ranges of (0.03419–0.06587), (0.7466–0.7833) and (0.1649–0.5665), respectively. The prediction superiority of SaDE-ELM was confirmed when it is achieved the lowest BIC value of − 293.40. Therefore, the proposed SaDE-ELM has demonstrated great potential to be used for on-site prediction, control and management of blast-induced ground vibration to prevent unwanted effects on the environment. Keywords Extreme learning machine · Ground vibration · Self-adaptive differential evolution · Blasting
1 Introduction Blast-induced ground vibrations are a major environmental impact of blasting that results from seismic waves moving through the ground. It is noteworthy that the resulting ground vibration after a blast is unavoidable. However, the magnitude of occurrence is of utmost concern as higher magnitude can cause damage to mining pit wall, cracks on buildings of the neighbouring community, disturbance to
humans and can even result in conflict between the mining company and the neighbouring community. Hence, there is a need to predetermine their level of occurrence before each blast is carried out through modelling and prediction. In modelling and prediction of blast-induced ground vibration, several approaches ranging from conventional empirical approaches [1–5] to the use of computational intelligence ([6–8] and references
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