A Machine Learning Centered Approach for Uncovering Excavators’ Last Known Location Using Bluetooth and Underground WSN

Abstract

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

“…In construction process field, 20 out of 61 observations were made regarding the construction of buildings, dams, roads, and tunnels. Within this field, the 60 out of 202 observations covered topics such as construction delays [22], crane, drilling and excavation tasks [14,18,24,39,48,70,74]; geological conditions [54], scaffolding collapse [68]; transport delays [31]; tunnelling [28,36,37,41,43,55,67]; workers and machinery location [34,40]. According to Erzaij et al [22], project suspensions are among the most persistent tasks facing the construction sector, due to the difficulty of the industry and the essential interdependence between the bases of delay risk.…”

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

“…Nevertheless, their technology has not been put into use or validated-just the general design has been shared. Kumari et al (2022) applied Decision Tree, KNN, Random Forest in their research in order to precisely locate trapped excavators or machines under-ground. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators' Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area.…”

“…One of the few papers in the wider field shows A Machine Learning-Based Method for Determining the Last-Known Location of Excavators (Kumari et al, 2022), while papers in the field of prediction of failure and stoppages are evidently not frequent. Due to supervised machine learning (SML) advantages, this paper is based on them.…”

Makine Öğrenmesi Yöntemleri Kullanılarak Öğrencilerin Kazanım Bilgileri ile Sınavlardaki Başarı Durumunun Tahmini

“…Doğrusal regresyon, bağımsız bir değişken ile bağımlı bir değişken arasındaki ilişkiyi tahmin etmek amacıyla kullanılan doğrusal bir yaklaşımdır. (Kumari vd., 2022) Doğrusala yakın ilişki gözlendiğinde, basit doğrusal regresyon modelleri denklem (1)'deki gibi ifade edilir (Mardikyan, 2005):…”