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Predicting Energy Consumption and CO_{2} Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

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## Abstract

**:**

_{2}emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO

_{2}emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator’s hourly energy consumption and CO

_{2}emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook’s data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO

_{2}emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO

_{2}emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO

_{2}emissions.

## 1. Introduction

_{2}emissions produced by the construction industry [7]. Within this industry, excavators are a major contributor to the emissions from heavy construction machines [8]. Consequently, excavation operations dominate in terms of total emissions from construction sites because of the prolonged usage of excavators during construction projects [9]. In an extensive study involving twenty-six types of construction equipment in the United States, excavators accounted for 15% of the total energy consumption and CO

_{2}emissions from construction equipment and machinery [10]. Excavators/backhoes are placed second in the top three contributors to CO

_{2}emissions (26%) for on-site construction in respect of total carbon emissions [11]. A 35% reduction in usage time of excavators leads to a reduction of approximately 15% in excavator emissions, and 10% of the total emissions of on-site construction [9]. Therefore, predicting the energy consumption and CO

_{2}emissions from excavators is critical to mitigate the environmental impact of earthwork operations [12].

_{2}) is considered to be the major component contributing to approximately 60% of the global warming effects [16]. In addition, since the early 1990s, CO

_{2}emissions have been the focus of taxation policies in the industrial sectors in most Scandinavian countries [17]. The Swedish Transport Administration (STA) has recently set the target to have a climate neutral infrastructure by 2045 in an effort to reduce both energy use and CO

_{2}emissions in infrastructure projects [18]. These goals will be transformed into procurement criteria on CO

_{2}emissions that the contractors need to fulfill and be able to estimate, and thus control when construction projects are in the early planning stages.

_{2}emissions of non-road diesel construction equipment [28], and using engine performance data to develop a model for estimating fuel consumed and emissions [29]. PEMS has been proposed as a framework to measure, monitor, benchmark, and possibly reduce the air pollution caused by construction equipment [30]. A portable exhaust emissions analyzer, SEMTECH DS by Sensors, has been used to measure the gaseous exhaust emissions from excavators [31]. Another option has been an estimating tool based on the productivity rate with fuel use rate and emission factors from the EPA’s NONROAD model that can estimate excavator emissions [32]. Similarly, there is an estimation taxonomy for fuel use and pollutant emissions rates of Non-road Construction Vehicles [33], and there is also the ENPROD MODEL for estimating the carbon footprint of heavy duty diesel (HDD) construction equipment [34].

_{2}emissions of excavators that can be used with limited information. This is largely because, at this stage of the operation, there are insufficiently detailed data regarding the construction process [39]. However, there are considerable general data available in respect of the quantity survey and geotechnical investigations during the pre-planning stage of construction projects. These data include parameters such as excavation depth, density of material excavation, bucket payload, default cycle time and horsepower for available excavators, and this information can be used as a primary data source to predict the hourly energy consumption and CO

_{2}emissions of excavators. Therefore, it becomes less expensive to consider the environmental impact at an earlier stage of construction projects [39,40] where available alternatives can be examined and the best selected.

_{2}emissions from excavators used in earthworks operations, thereby overcoming the problem of the shortage of detailed information from the construction process. In addition, the model would be able to indicate which factors have large impacts on the energy and CO

_{2}emissions from among all of the model’s input parameters, and provide insight into the relative importance of the output of the model for each of them (i.e., energy and emissions). This would then allow planners to compare different alternative excavators in order to reduce the likely CO

_{2}emissions from the construction work. The proposed model is based on artificial neural networks (ANN), and the study puts forward a mathematical formula for predicting the environmental impact of excavator operations based on the operational characteristics for different excavator models and the parameters of the earth excavation. The results from a multivariate linear regression (MLR) analysis of the same input and output parameters are compared with the results of the proposed ANN model, thus demonstrating the efficiency of the ANN model as a prediction formula. The model’s output can help planners to estimate the energy consumption and CO

_{2}emissions of the chosen excavators based on digging depth (D

_{p}), cycle time (T

_{c}), bucket payload (B

_{p}), bank density of excavation materials (B

_{d}), and horsepower of excavator engine (H

_{p}). In addition, planners can easily employ the results of the proposed model using an Excel spreadsheet, Matlab, or any other computational program, depending on their preference.

_{2}emissions of construction equipment, the current knowledge gap, and the contribution of the study. Section 2 describes the methodology for the proposed models and data generation, Section 3 contains results and discussion and, in the final section, the conclusions, limitation and future research directions are discussed.

## 2. Methodology of the Proposed Model for Forecasting the Energy Use and CO_{2} Emissions

#### 2.1. Extraction of A Database Based on the Excavator Manufacturer’s Handbook

#### 2.2. Collecting Mass Excavation Characteristics of Different Types of Earth

#### 2.3. Generating the Excavator Database Using Different Characteristics of Mass Excavation to Produce the Input Data for the ANN Model

_{t}), digging depths (D

_{p}), bucket sizes (B

_{s}), bucket payloads (B

_{p}), cycle times (T

_{c}), load factors (L

_{f}), and the engine horsepower of the excavator (H

_{p}). For example, with different types of earth excavation (e.g., packed earth, sand, gravel, and hard clay), each type has various values of earth density, and these values can be put into ranges as shown in Table 1, and then tested for different digging depths with a different bucket size and fill factor for each depth. To illustrate this, Equation (1) was used to calculate bucket payload based on bucket size (B

_{s}) for each model of excavator, together with bucket fill factor (B

_{f}), with a range of 0.65 to 1.1 (see Table 2), and based on the type and density of material being excavated and its shape when loaded in the bucket.

_{p}is the bucket payload representing the actual volume (m

^{3}) of material hauled by the excavator bucket (also referred to as heaped bucket capacity); B

_{s}is the design volume (m

^{3}) of the excavator bucket (also referred to as struck bucket capacity); and B

_{f}is the percentage of materials actually carried in respect of the excavator bucket’s available volume [41]. An extensive analysis procedure was undertaken, using Excel, to test twenty-five models of Caterpillar excavator with sixteen different values of bucket size within the group’s range, with four different values of bucket fill factor for each one, and different values of load factor based on different density values for each type of earth to be excavated. This analysis led to the production of 5092 rows of data in the database (i.e., each row has a unique set of values), each row having five columns. The final results of this analysis can be expressed as a matrix (with a dimension of 5092 × 5) in order to provide the input matrix for the ANN model. In addition, an energy consumption and CO

_{2}emissions database for each operational scenario of the excavators was created based on the principle equation from Filas 2002 [42]. This approach was proposed in order to estimate fuel consumption, relationships between fuel specifications, load factor (decimal), and engine horsepower (kW) for each excavator’s operational scenario. Equations (2) and (3) can be used to generate energy and emissions (CO

_{2}), and also to provide the output database matrices for the ANN and MLR models (with dimensions of 5092 × 1) for each of the energy and CO

_{2}emissions outputs.

_{d}and E

_{md}are, respectively, the energy consumption (MJ/h) and CO

_{2}emissions (kg/h) of the excavator. SFC is specific fuel consumption (0.22 kg/kW h) [43,44], to be set to a suitable value for engines with power in the range 28.8 to 370 kW [43]. H

_{p}is the horsepower of the excavator engine (kW), which represents the maximum power level designed for the excavator engine [45]. E

_{cf}is a conversion factor for the energy of each liter of diesel fuel (36 MJ/L) [46]. ρ

_{fuel}is the specific gravity of the diesel fuel to be consumed (0.85 kg/L) [47,48,49,50,51,52], ranging between 0.83 and 0.87 kg/L. E

_{mcf}is a conversation factor for the carbon dioxide (CO

_{2}) of each liter of diesel fuel (2.6569 kg CO

_{2}/L) [53]. L

_{f}is the engine load factor (decimal). The engine load factor is greatly affected by the usage patterns of the NONROAD engine [45], and typically this has a range of values depending on engine type and level of utilization [42]. However, this parameter was developed to identify the practical average proportion of engine rated horsepower used, based on work conditions, to take into account the effect of both idle and partial load situations when the machine is being operated [54]. Load factor values are used in Equations (2) and (3) to generate an energy and GHG emission database for the excavators, based on the approach mentioned by [35] in respect of terms described in the manufacturer’s handbook [41], and as described in [55,56], which refer to the density of the excavated material as “bank density”. Thus, a load factor database with material density values (i.e., bank density) was compiled from different sources, and this was then clustered based on three categorized groups, as shown in Table 1. Consequently, forty-two values of load factor with their density values were processed and analyzed using a first degree of exponential algorithms by fitting curve regression analysis to find an acceptable relationship between the two variables (see Equation (4)).

_{D}is the bank density (kg/m

^{3}) (i.e., the material density in its natural state before disturbance, either in place or in situ). The load factor formula is considered a good representation of the relationship between the densities and load factor based on a goodness of fit report that shows values for R-square of 0.9342, a minimum error of 5.7073 × 10

^{−4}, and a maximum error of 0.1292 for specific values of bank density (in the range 960–2415 kg/m

^{3}) and load factor (0.15–0.91). Figure 2 shows the flowchart for the generation of training data sets for the ANN model. Figure 3 shows an integrated definition for function modeling (IDEFO), which represents a simplified process for generating the energy consumption and CO

_{2}emission data of excavators used for earth-moving in construction projects. Table 2 shows the boundary conditions and range limits applied in order to test and analyze various characteristics for excavators related to earth type, thus generating a very large database.

#### 2.4. Designing the Predictive ANN Model with Forwards/Backwards Propagation Learning Algorithms

_{p}), total cycle time (T

_{c}), bucket payload (B

_{p}), horsepower of the excavator engine (H

_{p}), and load factor (L

_{f}). A second issue is that there should be no rule applied to determine the number of hidden layers in the ANN [57] since using more than one hidden layer can be considered to produce more filtering and weights modification of the ANN’s output [58]. Despite this, one hidden layer is used by most researchers for predicting objectives [59,60,61], this may be problematic for the expression of the final prediction formula, as complicated weightings result when there are many hidden layers [61]. A common practice for determining the number of nodes in the hidden layer is to use trial-and-error or experimentation [59] because there is no fully proven theoretical or algorithmic procedure to determine the nodes in the hidden layer [57,59,62]. In addition, investigative studies of ANNs have shown that the number of hidden layers has no significant effect on prediction performance [59]. In this study, one hidden layer was used in each prediction model (energy consumption and CO

_{2}emission).

_{2}emissions per hour of material hauled by the excavator as a single output parameter, together with one hidden layer with fifteen hidden nodes. Again, 90% of the data (i.e., 4629) were used in the training of the neural network, and the remaining 10% (i.e., 463) of the total data were used both for testing the constructed network and for verifying the final results. This model was also selected based on the minimum value of mean square error (0.00000895) produced by this combination (5-15-1) with R-values of 0.99970 and 0.99975 for training and testing output versus target respectively, at a level of learning rate of 0.1 and with 21 iterations to achieve an optimum representation. The architectural structure of the optimal ANN models is shown in Figure 4, showing three layers with their connections.

_{s}represents the normalizing/scaling value of the input data, x

_{i}is the value of input data for each parameter (i.e., 1, 2, 3,…, n), x

_{min}is the minimum value of the input data for each parameter, and x

_{max}is the maximum value of the input data for each parameter. In order to develop a prediction formula based on the best result from the ANN model, the values of weight connections and thresholds (i.e., bias) for input to hidden layer and hidden to output layer are essential elements in formulating a final expression for predicting energy consumption and CO

_{2}emissions per hour of excavators. The matrix representation of the prediction formula for both ANN models was preferred because it offers the simplest version for users and practitioners in the field. The following (Section 2.4.1 and Section 2.4.2) describe the matrices for weight connections between the input and hidden layers and the hidden and output layers, input parameters and bias values. In addition, mathematical operations were used on the matrices to produce a final estimation for energy and CO

_{2}emissions per hour of material hauled by an excavator.

#### 2.4.1. Matrix Expressions and Final Formula for Energy Prediction from the Proposed ANN Model

_{1}” is digging depth, “b

_{2}” cycle time, “b

_{3}” bucket payload, “b

_{4}” engine horsepower, and “b

_{5}” load factor), a matrix “C” represents the bias values (i.e., threshold) of nodes in the hidden layer (where c = 1, 2,…, p; p = 15), a matrix “H” represents the weights connection vector matrix between the hidden and output layers (where h = 1, 2,…, O; O = 15), and “θ

_{y}” represents the bias value (i.e., threshold) of nodes in the output layer.

_{1}; f

_{2}; f

_{3}; f

_{4}; f

_{5}; f

_{6}; f

_{7};……..; f

_{15}), “K” is a vector matrix for elements facing each other in both the “F” and “H” matrices (note that this step is not typical for matrix multiplication, but it is regarded as multiplication only for parallel elements in both of them), “S” represents the summation values of the bias value of the node output layer and the summation values for the elements of the “K” matrix (For i = 1, 2,…, n; n = 15), and “En

_{s}” represents a prediction value for excavator hourly energy consumption (MJ/h) of material excavated.

#### 2.4.2. Matrix Expressions and Final Formula for CO_{2} Emission Prediction from the Proposed ANN Model

_{2}emissions per hour of material hauled by excavators is based on the minimum error performances and robust values of the correlation coefficient for the proposed ANN model. Consequently, a matrix “AA” represents the weight connections matrix between the input and hidden layers (For i = 1, 2,…, n; j = 1, 2,…, m; n = 15 and m = 5), a matrix “B” represents scaled values for input parameters (where b = 1, 2,…, q; q = 5) (i.e., element “b

_{1}” is digging depth, “b

_{2}” cycle time, “b

_{3}” bucket payload, “b

_{4}” engine horsepower, and “b

_{5}” load factor), a matrix “CC” represents the bias values (i.e., threshold) of nodes in the hidden layer (where cc = 1, 2,…, p; p = 15), a matrix “HH” represents the weight connections vector matrix between the hidden and output layers (where h = 1, 2,…, O; O = 15), and “θ

_{yy}” represents the bias value (i.e., threshold) of nodes in the output layer.

_{1}; ff

_{2}; ff

_{3}; ff

_{4}; ff

_{5}; ff

_{6}; ff

_{7};……..; ff

_{15}]) and “HH” matrices (note that this step is not a typical matrix multiplication, but it is regarded as multiplication only for the parallel elements in both of them) (i = 1, 2,…, n; n = 15), “SS” represents the summation values of the bias value of the node output layer and the summation values for elements of the “KK” matrix, and “Em

_{s}” represents a prediction value for excavator CO

_{2}emissions per hour (kg/h) of material excavated.

#### 2.5. Relative Importance and Sensitivity Analysis of Excavator Input Factor on Energy Consumption and CO_{2} Emissions

_{2}emissions associated with the excavators. For instance, if we have two models of excavators that are different in terms of operational characteristics, we can estimate the environmental impact of each of them for each hour of material hauled and, by comparing them with environmental conditions and the productivity performance rate, select the optimum option. In 1991, Garson [67] proposed the partitioning weights method to determine the effects of different input parameters on the outputs, and this method was adopted by [68]. It has been used in this study in order to determine the relative importance of the various input parameters that impact on excavator energy consumption and CO

_{2}emissions per hour for various operational conditions within all operational scenarios that were tested with generated data.

_{2}emissions per hour for the excavator in both of the proposed ANN models was found to be the cycle time (T

_{c}), at 67.67% and 66.16% respectively, which involves excavating, swing, loading and returning to the digging start point. Of second highest importance was engine load factor (L

_{f}) (15.85% and 15.82%), followed by horsepower (H

_{p}) (7.08% and 7.30%), digging depth (D

_{p}) (4.98% and 5.86%), and bucket payload (B

_{p}) (4.51% and 4.85%), for both ANN models. This result is shown in Figure 6, where cycle time is a demonetized factor for outputs of both models.

## 3. Multivariate Linear Regression Formulae for Predicting Energy Consumption and CO_{2} Emissions Compared with ANN Models

_{p}, T

_{c}, B

_{p}, H

_{p}and L

_{f}for predicting excavator energy consumption and CO

_{2}emissions per hour of material excavated. The analysis results are shown in the following mathematical models (see Equations (18) and (19)). Both the energy and the emissions prediction formulae are based on the value R-square being 0.8647 (see Figure A3a,b).

_{R}” and “Em

_{R}” represent, respectively, hourly energy consumption and CO

_{2}emissions of material hauled by the excavator; “D

_{p}” is digging depth; “T

_{c}” is cycle time; “B

_{p}” is bucket payload; “H

_{p}” is horsepower; and “L

_{f}” is load factor. Intercept value “Α” and slope values “ß” for each formula are shown in Table 3.

## 4. Results and Discussion

_{2}emissions of excavators, and can be categorized thus: (1) the proposed ANN models for predicting energy consumption and CO

_{2}emissions; (2) identification of factors that have large impacts on the energy consumption and emissions; and (3) comparing the results of a multivariate linear regression formula with ANN model outputs to provide evidence for adopting ANN models as the optimum prediction formulae.

^{1/2}) [72]. Fletcher et al. (1993) stated the number of hidden nodes should be tested with intervals of ((2(n))

^{1/2}+ m) to (2n + 1) [73]. Hegazy et al. (1994) suggested the number of hidden nodes should be equal to one half of the total number of input and output parameters (i.e., 1.5 × (n + m)) [74], where n and m represent, respectively, the number of input and output parameters in all expressions given in this section. Although there are no strict rules that should be followed, based on these previous suggestions, the proposed range for the number of hidden nodes in the ANN prediction models should be between 3 and 11 as a guideline for finding the optimum number of hidden nodes. Therefore, the ANN prediction models were tested with different node numbers within the selected intervals, and showed good results with most of these numbers. However, the ANN models also showed a capacity to reduce the mean square error (MSE) for prediction values with an increasing number of hidden nodes. Consequently, the trials were extended to include thirteen and fifteen hidden nodes, with trial-and-error used to pick the optimum number [63]. Therefore, fifteen hidden nodes was seen to be the best design within the tested range of nodes for processing elements in each hidden layer in both of the ANN prediction models. This selection is also supported by the general rule proposed by Jadid et al. 1996, which gives the maximum number of nodes in the hidden layer [75]. Thus, the upper limit in this study is approximately 18 nodes, based on their rule for a range value of 10.

_{2}emission using the linear relationship between fuel consumption and CO

_{2}proposed by Wojciech G. et al. 1999 [76], and adopted by Yutong G. et al. 2007 [77].

_{2}emissions. Consequently, these values should be rescaled using Equation (20) to arrive at the actual prediction values for energy consumption and CO

_{2}emissions per hour.

_{r}represents the value of rescaling output data (i.e., actual prediction values), y

_{s}is the normalizing/scaling value of the output (i.e., the value calculated using the prediction Equations (11) and (17)), y

_{min}is the minimum value of the original output data, and y

_{max}is the maximum value of the original output data.

_{2}emissions of excavators in the earlier stages of construction projects. It allows an ideal plan to be designed which minimizes time lost during excavator operations over each cycle. Figure 7a,b shows the variation in the actual and predicted values (for both ANN and MLR) for energy consumption and CO

_{2}emissions for different excavator models for the various cycle times that work in the different site conditions , showing good agreement between the actual and predicted values for ANN models. It can be seen that the highest values of energy and emissions were produced from each excavator model at the longer cycle time for the same specific conditions and characteristics for each operational scenario. In addition, load factor was considered the second most important factor that impacted on the energy and emission of excavators by Mario et al. (2016) who showed the variation of load factor values in different operational scenarios [43]. However, load factor effects investigated for other heavy duty diesel equipment such as bulldozers have shown that a reduction 15% of load factor may have a significant effect on reducing fuel consumption and emission CO

_{2}[84].

_{2}emissions per hour, respectively. Furthermore, regression analysis can be considered as both evidence of, and a method to demonstrate, the validity of specific results of the prediction values from both of the proposed ANN models. The images in Figure 7a,b are constructed to demonstrate the accuracy of the ANN models in comparison with the MLR of various excavators in different work conditions. The x-axis represents the various work situations produces the different cycle-time for different excavators where each excavator operates at a specific cycle time, depth and earth type generating the engine load shown on the y-axis as energy or CO

_{2}emissions. In this case, based on the comparison between the actual values (i.e., the original data used to train both models) for energy and CO

_{2}emission with two predicted values (i.e., ANN and MLR), we can see consistency between the results of the ANN models with actual data for all operational scenarios through the specific ranges, while MLR results show divergent behavior with the actual data for several operational scenarios (see Figure 7a,b). Although these results are acceptable, based on the best value for R-square (0.8647) in both the regression models, it is still a linear model and does not represent an accurate picture of the complex connections between independent parameters. Thereby, this demonstrates the efficiency of the proposed ANN models as prediction formulae for use at an early stage of the construction process in the planning phase when there is a lack of detailed information. The ability of ANN to tackle complex relationships between independent variables that cannot be solved by more traditional methods has been demonstrated by other researches [85,86,87].

_{2}) in the early planning stages of construction projects despite the practicalities of the shortage of information and details about construction processes during this stage [39]. To overcome this shortfall, the availability of other details from preliminary surveys and investigations on geotechnical information, level/density of cutting layers, and the operational characteristics of the machines available to a construction company or contractor are employed. Furthermore, existing methods to estimate energy use and CO

_{2}emissions might need more detailed information and effort before application or the calculation of certain parameters or details, for instance, productivity rate [35,88], engine speed and other engine operational characteristics [3], in order for those formulas to be applied. The research’s results will thus be of interest to those planning and estimating earthmoving and similar operations in construction projects because it can provide an indication of energy consumption and CO

_{2}emissions before the construction phase commences.

## 5. Conclusions

_{2}emissions of excavators per hour of material hauled. To do this, data relating to energy consumption have been applied to artificial neural networks in order to model energy consumption and CO

_{2}emissions per hour for excavators. In each prediction model, five input parameters were used with one output parameter, with the ANN model proving that the neural network is capable of modeling and predicting with high accuracy. Moreover, the ANN model has shown the relative importance of the input parameters and their effects on the output. The cycle time of excavators is the dominant factor (≈67%) for levels of energy consumption and CO

_{2}emissions per hour of material hauled by the excavator; the load factor is the second most dominant factor (≈15.9%). Multivariate linear regression (MLR) analysis was carried out to confirm that the results from the ANN prediction models were the best prediction values. The ANN model has displayed an excellent correlation with independent parameters in respect of developing an efficient predictive formula that can compensate for the lack of construction process details when projects are in the early planning stages. The ANN prediction equations, in the form of matrices, are a good aid for planners and practitioners in construction project management when estimating energy consumption and CO

_{2}emissions for each hour of earth-moving in the early (i.e., planning) stage (i.e., limited details) of construction projects, and when selecting the optimum excavator for earth-moving while also considering environmental impacts.

_{2}emission depend on indirect measurement. The model presented here will be further extended to use different values of performance efficiencies for excavator fleets in order to cover all real-life operational scenarios where excavators are employed in earth-moving operations. Furthermore, study of other parameters that highly affect behavior under different conditions of earth density and bucket payload such as engine torque to compare the prediction efficiency of the proposed model in the case of use engine load factor or engine torque would be interesting. In addition, the model will be adapted consider other types of greenhouse gas (GHG) emissions and particulate matter.

## Author Contributions

## Conflicts of Interest

## Appendix A

**Figure A1.**(

**a**–

**f**) Graphical representation of the properties of the ANN model proposed for energy prediction.

**Figure A2.**(

**a**–

**f**). Graphical representation of the properties of the ANN model proposed for emissions prediction.

**Table A1.**Trials of different combinations within ANN model to select the optimal energy model performance.

Case | C * | N * | L * | S^{training} | S^{testing} | MSE | Epochs | R^{training} | R^{testing} |
---|---|---|---|---|---|---|---|---|---|

A | 1 | 5-3-1 | 0.1 | 4073 | 1019 | 0.00001974 | 79 | 0.99755 | 0.98361 |

2 | 5-3-1 | 0.1 | 4364 | 728 | 0.00007876 | 91 | 0.99715 | 0.99748 | |

3 | 5-3-1 | 0.1 | 4526 | 566 | 0.00007665 | 76 | 0.99718 | 0.99743 | |

4 | 5-3-1 | 0.1 | 4629 | 463 | 0.00007479 | 71 | 0.99724 | 0.99712 | |

5 | 5-3-1 | 0.1 | 4700 | 392 | 0.00026406 | 63 | 0.98964 | 0.98722 | |

6 | 5-3-1 | 0.1 | 4752 | 340 | 0.00000995 | 85 | 0.99967 | 0.99968 | |

B | 7 | 5-6-1 | 0.1 | 4073 | 1019 | 0.00000987 | 71 | 0.99755 | 0.98362 |

8 | 5-6-1 | 0.1 | 4364 | 728 | 0.00007876 | 64 | 0.99715 | 0.99748 | |

9 | 5-6-1 | 0.1 | 4526 | 566 | 0.00007665 | 81 | 0.99718 | 0.99743 | |

10 | 5-6-1 | 0.1 | 4629 | 463 | 0.00029514 | 78 | 0.98904 | 0.99048 | |

11 | 5-6-1 | 0.1 | 4700 | 392 | 0.00098040 | 38 | 0.96099 | 0.95960 | |

12 | 5-6-1 | 0.1 | 4752 | 340 | 0.00000994 | 11 | 0.99795 | 0.99821 | |

C | 13 | 5-7-1 | 0.1 | 4073 | 1019 | 0.00001660 | 38 | 0.99755 | 0.98361 |

14 | 5-7-1 | 0.1 | 4364 | 728 | 0.00007876 | 42 | 0.99715 | 0.99748 | |

15 | 5-7-1 | 0.1 | 4526 | 566 | 0.00007665 | 63 | 0.99718 | 0.99743 | |

16 | 5-7-1 | 0.1 | 4629 | 463 | 0.00007479 | 44 | 0.99724 | 0.99712 | |

17 | 5-7-1 | 0.1 | 4700 | 392 | 0.00007118 | 73 | 0.99723 | 0.99650 | |

18 | 5-7-1 | 0.1 | 4752 | 340 | 0.00001840 | 60 | 0.99053 | 0.98910 | |

D | 19 | 5-9-1 | 0.1 | 4073 | 1019 | 0.00001970 | 31 | 0.99910 | 0.99034 |

20 | 5-9-1 | 0.1 | 4364 | 728 | 0.00002102 | 26 | 0.99924 | 0.99934 | |

21 | 5-9-1 | 0.1 | 4526 | 566 | 0.00002886 | 22 | 0.99894 | 0.99887 | |

22 | 5-9-1 | 0.1 | 4629 | 463 | 0.00001811 | 28 | 0.99933 | 0.99940 | |

23 | 5-9-1 | 0.1 | 4700 | 392 | 0.00002936 | 25 | 0.99885 | 0.99865 | |

24 | 5-9-1 | 0.1 | 4752 | 340 | 0.00000976 | 16 | 0.99921 | 0.99921 | |

E | 25 | 5-11-1 | 0.1 | 4073 | 1019 | 0.00000928 | 27 | 0.99926 | 0.99357 |

26 | 5-11-1 | 0.1 | 4364 | 728 | 0.00001489 | 19 | 0.99946 | 0.99954 | |

27 | 5-11-1 | 0.1 | 4526 | 566 | 0.00003063 | 14 | 0.99887 | 0.99881 | |

28 | 5-11-1 | 0.1 | 4629 | 463 | 0.00001585 | 22 | 0.99942 | 0.99935 | |

29 | 5-11-1 | 0.1 | 4700 | 392 | 0.00001616 | 45 | 0.99937 | 0.99933 | |

30 | 5-11-1 | 0.1 | 4752 | 340 | 0.00001847 | 34 | 0.99939 | 0.99945 | |

F | 31 | 5-13-1 | 0.1 | 4073 | 1019 | 0.00001991 | 33 | 0.99879 | 0.99269 |

32 | 5-13-1 | 0.1 | 4364 | 728 | 0.00004490 | 67 | 0.99837 | 0.99840 | |

33 | 5-13-1 | 0.1 | 4526 | 566 | 0.00001674 | 42 | 0.99938 | 0.99944 | |

34 | 5-13-1 | 0.1 | 4629 | 463 | 0.00001441 | 25 | 0.99947 | 0.99951 | |

35 | 5-13-1 | 0.1 | 4700 | 392 | 0.00001963 | 42 | 0.99873 | 0.99805 | |

36 | 5-13-1 | 0.1 | 4752 | 340 | 0.00000970 | 43 | 0.99897 | 0.99912 | |

G | 37 | 5-15-1 | 0.1 | 4073 | 1019 | 0.00000999 | 14 | 0.99964 | 0.99967 |

38 | 5-15-1 | 0.1 | 4364 | 728 | 0.00000930 | 43 | 0.99969 | 0.99971 | |

39 | 5-15-1 | 0.1 | 4526 | 566 | 0.00000925 | 17 | 0.99968 | 0.99969 | |

40 | 5-15-1 | 0.1 | 4629 | 463 | 0.00000851 | 15 | 0.99972 | 0.99974 | |

41 | 5-15-1 | 0.1 | 4700 | 392 | 0.00000944 | 19 | 0.99962 | 0.99973 | |

42 | 5-15-1 | 0.1 | 4752 | 340 | 0.00000937 | 47 | 0.99968 | 0.99968 |

^{training}= Size of data subset training; S

^{testing}= Size of data subset testing; MSE = Mean square error for best training performance; Epochs = number of iterations required to produce best output; R

^{training}= Correlation coefficient for output training data subsets (output vs. target); R

^{testing}= Correlation coefficient for output testing data subsets (output vs. target).

**Table A2.**Trials of different combinations within ANN model to select the optimal emissions model performance.

Case | C * | N * | L * | S^{training} | S^{testing} | MSE | Epochs | R^{training} | R^{testing} |
---|---|---|---|---|---|---|---|---|---|

A | 1 | 5-3-1 | 0.1 | 4073 | 1019 | 0.00001990 | 41 | 0.99755 | 0.98361 |

2 | 5-3-1 | 0.1 | 4364 | 728 | 0.00007876 | 69 | 0.99715 | 0.99748 | |

3 | 5-3-1 | 0.1 | 4526 | 566 | 0.00007665 | 66 | 0.99718 | 0.99743 | |

4 | 5-3-1 | 0.1 | 4629 | 463 | 0.00007479 | 71 | 0.99724 | 0.99712 | |

5 | 5-3-1 | 0.1 | 4700 | 392 | 0.00026406 | 63 | 0.98964 | 0.98722 | |

6 | 5-3-1 | 0.1 | 4752 | 340 | 0.00000996 | 84 | 0.99967 | 0.99968 | |

B | 7 | 5-6-1 | 0.1 | 4073 | 1019 | 0.00008863 | 53 | 0.99755 | 0.98362 |

8 | 5-6-1 | 0.1 | 4364 | 728 | 0.00007876 | 64 | 0.99715 | 0.99748 | |

9 | 5-6-1 | 0.1 | 4526 | 566 | 0.00007665 | 68 | 0.99718 | 0.99743 | |

10 | 5-6-1 | 0.1 | 4629 | 463 | 0.00029514 | 78 | 0.98904 | 0.99048 | |

11 | 5-6-1 | 0.1 | 4700 | 392 | 0.00098040 | 38 | 0.96099 | 0.95960 | |

12 | 5-6-1 | 0.1 | 4752 | 340 | 0.00002858 | 48 | 0.99795 | 0.99821 | |

C | 13 | 5-7-1 | 0.1 | 4073 | 1019 | 0.00009781 | 71 | 0.99755 | 0.98361 |

14 | 5-7-1 | 0.1 | 4364 | 728 | 0.00007876 | 64 | 0.99715 | 0.99748 | |

15 | 5-7-1 | 0.1 | 4526 | 566 | 0.00007665 | 70 | 0.99718 | 0.99743 | |

16 | 5-7-1 | 0.1 | 4629 | 463 | 0.00007479 | 74 | 0.99724 | 0.99712 | |

17 | 5-7-1 | 0.1 | 4700 | 392 | 0.00007118 | 73 | 0.99723 | 0.99650 | |

18 | 5-7-1 | 0.1 | 4752 | 340 | 0.00009549 | 42 | 0.99053 | 0.98910 | |

D | 19 | 5-9-1 | 0.1 | 4073 | 1019 | 0.00009714 | 49 | 0.99910 | 0.99034 |

20 | 5-9-1 | 0.1 | 4364 | 728 | 0.00002102 | 62 | 0.99924 | 0.99934 | |

21 | 5-9-1 | 0.1 | 4526 | 566 | 0.00002886 | 22 | 0.99894 | 0.99887 | |

22 | 5-9-1 | 0.1 | 4629 | 463 | 0.00001811 | 28 | 0.99933 | 0.99940 | |

23 | 5-9-1 | 0.1 | 4700 | 392 | 0.00002936 | 39 | 0.99885 | 0.99865 | |

24 | 5-9-1 | 0.1 | 4752 | 340 | 0.00001938 | 69 | 0.99921 | 0.99921 | |

E | 25 | 5-11-1 | 0.1 | 4073 | 1019 | 0.00000999 | 26 | 0.99926 | 0.99357 |

26 | 5-11-1 | 0.1 | 4364 | 728 | 0.00001489 | 37 | 0.99946 | 0.99954 | |

27 | 5-11-1 | 0.1 | 4526 | 566 | 0.00003063 | 45 | 0.99887 | 0.99881 | |

28 | 5-11-1 | 0.1 | 4629 | 463 | 0.00001585 | 28 | 0.99942 | 0.99935 | |

29 | 5-11-1 | 0.1 | 4700 | 392 | 0.00001616 | 41 | 0.99937 | 0.99933 | |

30 | 5-11-1 | 0.1 | 4752 | 340 | 0.00000938 | 24 | 0.99939 | 0.99945 | |

F | 31 | 5-13-1 | 0.1 | 4073 | 1019 | 0.00000963 | 18 | 0.99879 | 0.99269 |

32 | 5-13-1 | 0.1 | 4364 | 728 | 0.00001990 | 27 | 0.99837 | 0.99840 | |

33 | 5-13-1 | 0.1 | 4526 | 566 | 0.00001674 | 29 | 0.99938 | 0.99944 | |

34 | 5-13-1 | 0.1 | 4629 | 463 | 0.00001441 | 25 | 0.99947 | 0.99951 | |

35 | 5-13-1 | 0.1 | 4700 | 392 | 0.00003263 | 42 | 0.99873 | 0.99805 | |

36 | 5-13-1 | 0.1 | 4752 | 340 | 0.00000923 | 52 | 0.99897 | 0.99912 | |

G | 37 | 5-15-1 | 0.1 | 4073 | 1019 | 0.00000993 | 28 | 0.99964 | 0.99967 |

38 | 5-15-1 | 0.1 | 4364 | 728 | 0.00000930 | 43 | 0.99969 | 0.99971 | |

39 | 5-15-1 | 0.1 | 4526 | 566 | 0.00000925 | 17 | 0.99968 | 0.99969 | |

40 | 5-15-1 | 0.1 | 4629 | 463 | 0.00000895 | 21 | 0.99970 | 0.99975 | |

41 | 5-15-1 | 0.1 | 4700 | 392 | 0.00000944 | 19 | 0.99962 | 0.99973 | |

42 | 5-15-1 | 0.1 | 4752 | 340 | 0.00000966 | 23 | 0.99968 | 0.99968 |

^{training}= Size of data subset training; S

^{testing}= Size of data subset testing; MSE = Mean square error for best training performance; Epochs = number of iterations required to produce best output; R

^{training}= Correlation coefficient for output training data subsets (output vs. target); R

^{testing}= Correlation coefficient for output testing data subsets (output vs. target).

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**Figure 2.**Flowchart for generating the training data sets of energy consumption and CO

_{2}emission.

**Figure 4.**Architectural structure for the optimal ANN model to predict energy consumption or CO

_{2}emissions.

**Figure 5.**Architectural structure to identify the relative importance of input parameters to the energy consumption or CO

_{2}emission ANN models.

**Figure 6.**Relative importance of input parameters to the energy consumption and CO

_{2}emission ANN models.

**Figure 7.**(

**a**,

**b**) Actual and predicted behavior of energy consumption and emissions for excavators with various operating conditions.

Caterpillar Excavator Model | Suitable Type of Earth | Bank Density (kg/m^{3}) |
---|---|---|

307C, 308D CR, 308D CR SB, 311D LRR, 312D, 312D L, 315D L, 319D L, 319D LN | Decomposed Rock-Packed Earth | 960–2260 |

M313D, M315D, M316D, M318D, M322D | Sand/Gravel | 1370–2082 |

320D, 320D RR, 321D CR, 323D, 324D, 328D LCR, 329D, 336D, 345D, 365C L and 385C | Hard Clay | 1089–2415 |

Excavator Model | D_{p} (m) | T_{c} (min) | B_{s} (m^{3}) | B_{f} (%) | H_{p} (kW) | L_{f} (%) |
---|---|---|---|---|---|---|

307C, 308D CR, 308D CR SB, 311D LRR, 312D, 312D L, 315D L, 319D L, 319D LN | 1.5–3.0 | 0.22–0.28 | 0.37–1.05 | 0.8–1.1 | 41–93 | 0.15–0.91 |

M313D, M315D, M316D, M318D, M322D | 2.0–4.0 | 0.17–0.23 | 0.8–1.37 | 0.9–1.0 | 95–123 | 0.15–0.91 |

320D, 320D RR, 321D CR, 323D, 324D, 328D LCR, 329D, 336D, 345D, 365C L and 385C | 2.3–5.6 | 0.23–0.35 | 1.05–5.0 | 0.65–0.95 | 103–355 | 0.15–0.91 |

Items | A | ß_{1} | ß_{2} | ß_{3} | ß_{4} | ß_{5} | |
---|---|---|---|---|---|---|---|

En_{R} | n | −562.921 | −5.79 × 10^{−9} | 8.14 × 10^{−9} | 3.25 × 10^{−10} | 4.45274 | 1177.95 |

Em_{R} | m | −42.1721 | 1.51 × 10^{−10} | 3.16 × 10^{−9} | 6.31 × 10^{−10} | 0.33359 | 88.2478 |

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**MDPI and ACS Style**

Jassim, H.S.H.; Lu, W.; Olofsson, T.
Predicting Energy Consumption and CO_{2} Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model. *Sustainability* **2017**, *9*, 1257.
https://doi.org/10.3390/su9071257

**AMA Style**

Jassim HSH, Lu W, Olofsson T.
Predicting Energy Consumption and CO_{2} Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model. *Sustainability*. 2017; 9(7):1257.
https://doi.org/10.3390/su9071257

**Chicago/Turabian Style**

Jassim, Hassanean S. H., Weizhuo Lu, and Thomas Olofsson.
2017. "Predicting Energy Consumption and CO_{2} Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model" *Sustainability* 9, no. 7: 1257.
https://doi.org/10.3390/su9071257