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Risk probability predictions for coal enterprise infrastructure projects in countries along the Belt and Road Initiative
International Journal of Industrial Ergonomics 69 (2019) 110–117
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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Risk probability predictions for coal enterprise infrastructure projects in countries along the Belt and Road Initiative
T
Hongxia Lia, Yixin Huangb,∗, Shuicheng Tianc a
School of Management, Xi'an University of Science and Technology, Xi'an, 710054, China School of Energy, Xi'an University of Science and Technology, Xi'an, 710054, China c School of Safety, Xi'an University of Science and Technology, Xi'an, 710054, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Risk prediction Improved artificial fish swarm algorithm (IAFSA) Cerebellar model articulation controller (CMAC) Barapukuria coal mine The Belt and Road initiative
The Belt and Road Initiative has significantly promoted the construction and development of coal enterprises along the Belt and Road through the building of a new platform that encourages global economic cooperation. This study examines problems caused by the uncertainty of risk occurrence during the process of coal enterprise construction as part of the Belt and Road Initiative. Consequently, the study identifies and classifies various risk factors. In total, 11 types of risks and 39 risk factors involved in the construction process are summarized. These include natural, cultural, religious, marketing, and outsourcing-related risks. A prediction indicator system was also established to apply to the risk occurrence probability related to coal mine construction along the Belt and Road. In this regard, the study embedded an improved artificial fish swarm algorithm (IAFSA) into a cerebellar model articulation controller (CMAC) neural network model and collected risk probability details for the construction of the Barapukuria Coal Mine in Bangladesh from 2013 to 2018 as sample data. By researching the model and sample, this study obtained various risk occurrence probability intervals. Moreover, diversified risk probabilities were verified and predicted. Finally, this study empirically proves that an IAFSA–CMAC parallel coupling algorithm is able to achieve precise predictions about risks. This finding has great significance for risk management and control in the coal enterprises of countries along the Belt and Road.
1. Introduction The initiative to jointly build the Silk Road Economic Belt and the Twenty-First Century Maritime Silk Road (namely, the Belt and Road Initiative) has great significance for the promotion and realization of a community of common destiny for coal enterprises from different countries. Countries along the Belt and Road have abundant coal resources. Indeed, 15 countries have more than 10 billion tons of coal reserves, accounting for 46.9% of the world's total. Owing to the infrastructure project's characteristics, which are non-recyclable, irreversible, and long term (Boyles et al., 2017; Monaghan, 2017), complex and frequent risks may occur during implementation. These risks may cause immeasurable losses for coal mining enterprises. This problem is common among coal mining enterprises, especially those in the Belt and Road Initiative countries, when infrastructure projects are constructed (Fang, 2016; Hao et al., 2017; Ma and University, 2015; and Xu et al., 2017). For example, in 2013, the Dahaize Mining Industry in Shaanxi, China, was forced to cease production because of frequent safety violations and accept inspections. Similarly, in 2015, some coal
∗
mining projects in Pakistan were shelved owing to disputes related to energy ownership. This current study researches the minimization of the risk occurrence probability in coal mining in the countries along the Belt and Road. Other experts (Lin et al., 2018; Moreno and Cozzani, 2018; Tavakolan et al., 2017) proposed a comprehensive approach to risk identification in accordance with the evaluation of occurrence probability. Their studies contributed to a control mechanism of shared regional capital cooperation and targeted risks, thereby providing effective support to reduce risk occurrence probability. Meanwhile, many other experts (Hao et al., 2017; Lai and Guo, 2017) have conducted useful research on risk assessment and prediction for engineering projects in the Belt and Road Initiative countries, thereby giving theoretical support for risk management and the control of energy enterprises' infrastructure projects. However, following research under the special circumstances of the Belt and Road Initiative on numerous relevant countries and cities, it is clear that the speed of risk changes is much higher than that of risk evaluation. This situation has resulted in less predicted data of the risk frequency of occurrence than required. Therefore, an easier algorithm for risk probability prediction is urgently
Corresponding author. E-mail addresses: [email protected] (H. Li), [email protected] (Y. Huang), [email protected] (S. Tian).
https://doi.org/10.1016/j.ergon.2018.10.006 Received 3 May 2018; Received in revised form 22 August 2018; Accepted 31 October 2018 0169-8141/ © 2018 Elsevier B.V. All rights reserved.
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north, offering direct services to the capital, Dhaka. The region is part of the Ganges alluvial plain, which consists of sediments deposited by the Ganges and Jamuna rivers. The terrain of the area is flat, with an elevation of +29 m–32 m. The north is slightly higher than the south, but the gradient is very low.
needed. Such an algorithm should also have faster convergence and excellent generalization ability with the aim of improving the theoretical system for risk management of infrastructure projects in countries along the Belt and Road. A great deal of research exists on the artificial fish swarm algorithm (AFSA) and the cerebellar model articulation controller (CMAC) neural network. Zhang et al. (2017) presented a Pareto improved AFSA (IAFSA) to solve the multi-objective disassembly line-balancing problem with fuzzy disassembly times. He et al. (2015) proposed a novel AFSA for solving large-scale reliability redundancy allocation problems (RAPs). Moreover, because the CMAC is a neural network that features partial approximation, speediness, and briefness, it enables the study of random multidimensional nonlinear fitting. Neurophysiology based on CMAC was proposed by J. S. Albus in 1975 and was later applied to various areas. Tsai and Yeh (2009) developed a CMAC neural network based on a control system for a speed-sensorless induction motor. The current study considers the neural network theory and the adaptive functions and faster convergence of related models, to develop a more suitable methodology for mining risk assessment. The structure related to the theory is proposed as follows.
2.2. Framework The framework of risk prediction for the Barapukuria Coal Mine is shown in Fig. 2 in five parts as follows: 1) risk identification; 2) data collection; 3) data preprocessing; 4) model building; and 5) risk prediction. First, according to related indicators in the BP-Energy Statistics Yearbook (2017), we analyze and review the literature by the expert scoring method. We then determine the final risk index by the fuzzy clustering method and employ it to consider risk influence factors. With the latter, we construct a new standard for our research, referring to the risk measuring standards specified in Coal Mine Project Risk Investment Guidance (2017). Second, the data, derived from the site by workers and managers, were collected. The workers and managers recorded the accident types and the numbers of each accident. Then, they divided the numbers according to the days in a year to obtain a decimal figure, which is the frequency of risk. Third, we construct a risk prediction model featuring timeliness and adaptability. We also consider the particularity of risk prediction in countries along the Belt and Road. We conduct normalized analysis after a pre-process to fill in the missing data by introducing linear regression and the collection of multi-risk isomerous data (structured and unstructured data). Based on the foregoing, and combined with the neural network theory and an IAFSA, a traditional CMAC neural network theory model was improved into a new CMAC model suitable for the risk prediction of projects along the Belt and Road.
- In the section 2, we discuss recent research to present the background and progress of mining infrastructure risk probability research. The current problem is also outlined. - Section 3 combines a literature review with a description of the understanding of risk characteristics and how they apply in different areas. We identify the most important influential and effective factors and build an index system based on factor identification. - In section 4, the mining infrastructure characteristics are combined with neural network theory to build the risk probability model in terms of the Barapukuria Coal Mine scenario. - In accordance with our research results, conclusions and improvement measures are given in section 5.
2.3. Improved methods
2. Materials and methods
2.3.1. Improving AFSA Generally, there are two approaches to weight adjustment for CMAC: the learning approach, which used the Jacobi iterative method in linear algebra, and the rotation approach, which uses the Gauss–Seidel iterative method. However, the weight calculated by either of these methods is a value instead of a range of values. Thus, by applying the clustering characteristics of artificial fish swarms, we calculated and predicted the interval range of risk probability and avoided risk fluctuations probably caused by time or conduction coefficients that consider the specificity of risk probability. We then optimize the artificial fish to achieve global optimization (Gong and Zeng, 2016; Wei et al., 2014). The IAFSA was used to adjust the weight size and calculate the weight range for each risk. The state of an individual artificial fish, Is(t), at time t is defined as
2.1. Study area Many cities and countries are involved in the Belt and Road Initiative. Unfortunately, the current difficult hydrogeological conditions, high ground temperatures, and complex cultural environments are rarely identified in most risk assessment reports. The result is undesirable factors in mining. Bangladesh is located along the Belt and Road. There is a mine in the country named the Barapukuria Coal Mine. Its advantages include convenient transportation, thick coal seams, abundant reserves, goodquality coal, a simple geological structure, and low biogas volume. Thus, the Barapukuria Coal Mine is a typical energy infrastructure project along the Belt and Road. It is also the first large-scale modernized mine developed and constructed in Bangladesh. Unexpectedly, its risk prediction has not been discussed in detail in studies about the mine's risk assessment. Thus, it is useful to take the Barapukuria Coal Mine (see Fig. 1) as an illustrative example. The productive capacity of Barapukuria Coal mine is 1.0 Mt/a. It is located between the latitudes of 25°31′ and 25°34′ N and 88°57′ and 88°59’ E in the northwest of Bangladesh, near Parbatipur in Dinajpur District. Within the surrounding area, transportation is quite convenient. The region is crossed by a broad-gauge railway from south to north on the western boundary, acting as an interchange between the north–south broad-gauge and the east–west meter-gauge railways. Approximately 5 km south of the mine, there is an east–west highway that eastwardly connects to another national highway. The latter goes 5 km northwest to Dinajpur, the provincial capital, and 75 km south to a northwestern city called Bogra along that highway. Moreover, located 70 km east of the mine, the Jamuna River stretches from north to south, reaching many domestic ports. Saidpur Airport is 10 km toward the
Is (t ) = {I1s (t ), I2s (t ), … Iωsi (t ) }, s = 1,2, …, AF _number
(1)
where AF_number represents the scale of the artificial fish swarm, and w represents the weight range. The CMAC has a hidden layer network; as such, the initial setting input layer X set has x inputs, the number of dimensions w is hn, and the number of output layer Y sets is m. The formula is w = h1(m+1) + h2(h1+1) + n(h2+1)
(2)
To ensure that the weights debugged by the AFSA match the generalization ability of the CMAC model, the AF values should be ordered from bottom to top in accordance with the weight distribution of the CMAC model. The first few sets of data with the highest risk activity are selected first; then the sets of data with the lowest risk activity. Thus, after several rounds of rotation, the state of the sth AF can be indicated by a value or a range of values as follows: Is(t)(i = 1, 2, …, w), 111
(3)
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Fig. 1. The location and geologic and tectonic setting of the Barapukuria Coal Mine (modified from Howladar et al., 2014).
Fig. 2. Risk prediction network framework model of CMAC.
process, it is impossible for all the risks of energy enterprise infrastructure projects in the Belt and Road countries to be triggered at once. Thus, an element, xi, inputted into space X simultaneously activates all yn types of risk in the real space, A. This situation causes the superposition value of yn risk weights to be 1. Other risks that do not occur are zero. Here, n is C, the generalization ability of the CMAC network. The explanation for each layer is as follows. The first layer is the input layer. Factors in the “virtual” space, X, are mapped according to discrete points in the real space, A. Then, X = {x1, x2, …, xn} is quantified and performed for data preprocessing. If a component of the risk factor occurrence probability of each risk can be quantified as a value, q, the set of components of the n risk factors can become a risk state-space, qn. As such, the quantification formula of the specific risk occurrence probability is
Where, Xs(t) indicates the state of the AF. The difference in generalization ability controls the size of the AF, while fluctuations of the AF influence wn. The variable wn is ωi in the CMAC model. 2.3.2. The IAFSA embedded in the CMAC model The CMAC network model is considered an “associative controller” network model, which is similar to a neural cerebellar cortical structure. Fig. 2 shows the network structure of the two-dimensional input/ one-dimensional output CMAC risk probability prediction model constructed in this study. The area labeled X in Fig. 2 represents the occurrence probability of a set of risk factors with an n-dimensional input, which is the “virtual” space of the input variable. The real space, A, represents the “storage units” with m types of risk. X={x1, x2, …, xn}T indicates all n-dimensional input components in the area X. It also represents the characteristic components of the risk and is referred to as the influencing factors that trigger risk. Y={y1, y2, …, yn}T indicates the eigenvector corresponding to the output variable. In the actual
Xp = {x1, x2, …, xn}T, n = 1, 2, …, n, p
(4)
Where X represents a set of risk factors and xn represents the number of 112
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coal market demand, mining's competitiveness, and price changes for coal (and coal chemical products).
components in the eigenvector. The second layer is the middle layer. To perform the unique mapping of the virtual space, Xp, onto the real space Yp, the number of “storage units” in the real space, Yp, must be greater than or equal to the number of states in the “virtual” space, Xp. Thus, the formula is m ≥ qn
3.1.3. Technical risk Normally, technical risk relates to construction's particularity, technological advancement, and equipment reliability. Thus, multiplefactor analysis is required for specific technical risk problems. For example, some factors involved in technological advancement, applicability, reliability, and reasonability must be considered during construction.
(5)
Where m represents the number of storage unit states in Yp and qn represents n numbers of characteristic components. Thus, the input relationship of the second layer is as follows. Input relationship: Xp={x1, x2, …, xn}T, n = 1, 2, …, n
3.1.4. Geological and technical mining conditions risk Contrary to expectations, diverse geological structures and engineering conditions may result in risks related to geological and technical mining conditions. The threat of the risks may lead to increased investment or construction extensions. The risk factors include hydrogeological engineering conditions, coal seam conditions, spontaneous combustion, and gas.
(6)
Output relationship Y ={y1, y2, …, ypn}T, p = 1, 2, …, qn; n = 1, 2, …, n p
(7)
p
Where Y represents a set of risk factors, yn represents the number of components in the eigenvector, and p represents the number of statespaces in the set of characteristic components. It is then possible to use hash coding to map the real space, A, which has qn storage unit addresses, onto a compressed space, Yp, with physical addresses. The third layer is the output layer (risk probability prediction layer). Through the assessment of risk as a combination of characteristic components in the first layer, and the unique mapping of the “virtual” space, Xp, onto the real space, Yp, the CMAC network risk output is the weight value of the risk storage that has gone through learning and accumulated to 1, achieving a prediction for risk probability (extent of loss). Thus, the network output of a certain input, Xi, is
P (Y p) = F (x ) =
∑
wi
i=Y ∗
3.1.5. Economic foundation risk Economic foundation risk mainly includes the exchange rate, the interest rate, and funding sources. Because funds for a mining construction project are always in demand, a funding shortage or interruption may lead to a project's delay or even termination. Interest rate and exchange rate changes may also have a significant impact on financing costs. For example, if interest rates and exchange rates rise, financing costs will rise simultaneously, resulting in increased costs and reduced profit margins. 3.1.6. Organizational management risk Irrationalities may occur during planning, construction, and coordination, and in human resources management, thereby affecting project implementation schedules and operational efficiency. These irrationalities refer to organizational management risk, which includes unreasonable organizational structure and incomplete regulations and supervision.
(8)
where Y* represents the number of risks that may occur and ωi represents the corresponding weight of the risk. 3. Results analysis
3.1.7. Safety risk A safety risk is a problem that has an impact on a project's sustainable development. Such risk is classified into the traditional safety risk of an accident and non-traditional risk. The former includes equipment failure, serious injury, and mortality. The latter includes riots, armed conflicts, and wars.
3.1. Risk classification Relative studies have combined with the reasons for energy enterprises’ risks in the Belt and Road Initiative area and considered research reports. Fuzzy clustering is used to classify risks into 11 categories and identify 39 risk factors that trigger various types of risks. Fig. 3 shows that as a risk identification system, the data visualization that is used to perform risk identification for energy enterprise infrastructure projects along the Belt and Road. Mining risks normally occur during the stage of early feasibility studies, project construction, and operations. Several foreign projects’ published reports and research results have shown that, during the construction and operation stages, there is little probability of risk; risk is more likely to be seen at the stage of feasibility studies. In addition, other types of risks vary at different phases. Thus, we conclude that specific kinds of risks exist as follows.
3.1.8. External conditions risk External conditions risk directly influences coal mines’ efficiency at the operational stage. The risks include carriage conditions, transportation, water and power supply, and public relations. 3.1.9. Social stability risk Social stability risk refers to a project in the process of construction or already running that may be affected by its social environment. In addition to conventional factors, public security and riots are included in social stability risk. Table 1 shows the risk classification in a large mining industry. The Belt and Road Initiative area, as outlined earlier, uses the risk identification of energy enterprise infrastructure projects and the risk classification standards in The Guideline of Construction Project Feasibility Study. The frequencies of risk and its factors are divided into five levels, which is the theoretical basis for data samples and risk probability prediction.
3.1.1. Policy risk Policy risk refers to the impact of national macroeconomic policy on project construction. Such risk includes changes under existing political and economic conditions and energy policy adjustments. Risk may also be affected by compliance with relevant policies. 3.1.2. Marketing risk Marketing risk is among the major risks often encountered by competitive projects. It is mainly manifested as poor sales of coal products, product prices being in the doldrums, and failure to achieve production and sales revenue goals. The main influential factors are
3.2. Data normalization 3.2.1. Missing data Data may be missing during the collection process, which can be 113
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Fig. 3. Coal enterprises' infrastructure projects involved in 11 categories and 39 risk factors.
respectively. We also assume that the sample data of risk eigenvector Xi is classified in level k, where the upper and lower limits are (A)ik and Bik yik respectively. Finally, we assume Mik is the corresponding classification level of the range of values between the upper and lower limits of the risk factor's characteristic component and risk probability. Then, the stochastic simulation formula for the occurrence probability of that risk factor is
Table 1 Risk occurrence frequency level (unit: %). Risk probability level
Low
Medium–low
Medium
Medium–high
High
Occurrence frequency
0–20
20–40
40–60
60–80
≥80
replaced by interpolation generation. Taking into account the classification levels of risk occurrence frequency and risk factors, this method employs the stochastic distribution of the risk probability of an energy infrastructure project in an area selected for prediction. Such samples and their classification levels are used to construct the model's initial sequence. We assume the risk characteristic component (risk factor), xi, is classified in level k, where the upper and lower limits are aik and bik
P(x, y)=(xkij, yki),
(9)
Where P represents the risk occurrence frequency, j represents the sample sequence capacity generated by a certain classification level, i represents certain risk, and k represents the number of classification levels. According to the model above, the risk occurrence frequency of classification level k produces nk groups of data samples. 114
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3.2.2. Normalization A total of 39 risk factors and 11 risk indicators are in the prediction of risk probability for energy infrastructure projects. Because a big difference exists between each indicator's units and dimensions, all the risk indicators are normalized when calculated in order to minimize the impact and magnitude of such differences. The normalization equation is X*i
= Xi / Xmax × 100%,
Table 3 Partial normalization for risk factors’ uniform values at the Barapukuria Coal Mine. Group number
1 2 3 4 5
(10)
X*i
represents the normalized risk indicator, Xi represents the Where normalized risk factor value, and X-max represents the maximum risk factor indicator in the sample.
Risk factors' uniform values y1
y2
y3
y4
y5
y6
y7
y8
yn
0.28 0.00 0.54 0.32 1.00
0.45 0.64 1.00 0.22 0.00
0.37 0.00 0.10 0.32 1.00
0.00 1.00 0.62 0.36 0.62
1.00 0.09 0.62 0.21 0.00
0.00 0.55 0.26 1.00 0.73
0.73 1.00 0.14 0.00 0.29
0.61 1.00 0.03 0.00 0.79
… … … … …
Data source: Investment Feasibility Analysis of the Barapukuria Coal Mine in Bangladesh.
3.3. Risk prediction and data analysis types of risks and 39 risk factors, while the output is the occurrence probability calculated for each risk factor. Using the generalized parameter C = 11 and the occurrence probability as the output, the five groups with the highest risk and the five groups with the lowest risk are employed as training samples. The remaining samples are used for prediction.
Based on the classification levels of risk and risk factors, the occurrence frequency of different risks from 2012 to 2017 is extracted from the Manual of Infrastructure Construction (Phase III) of the Barapukuria Coal Mine in Bangladesh. A risk prediction is performed for the Barapukuria Coal Mine, providing it with a new reference for future development and operations.
3.3.4. Verification The interval of the solution's total risk occurrence probability is compared with the actual total risk probability in 2016. The relative error can then be calculated. This approach enables the volatility difference of risk occurrence to be a floating coefficient in future predictions (see Table 5). Based on this risk prediction model, the risk occurrence probability of mining infrastructure projects in the Barapukuria Coal Mine from 2018 to 2023 is predicted using the sample data in the remaining 10 groups of risk and risk factors, thereby providing a reference for decision-makers. The actual evaluation and prediction results are shown in Fig. 5. The results in Fig. 5 show that the risk occurrence probabilities for 2018–2023 are 0.3543–0.4628, 0.2937–0.3633, 0.3714–0.4553, 0.4111–0.5371, 0.4273–0.5311, and 0.3682–0.5017 respectively. These are considered low risk, medium–low risk, medium–low risk, medium risk, medium risk, and medium–low risk levels respectively. The six groups of data highlight that there may be a higher risk probability for the coal enterprise infrastructure in Bangladesh within the next six years. The factors include natural disaster risk, social stability risk, cultural and religious difference risk, investment risk, non-traditional security risk, and outsourcing conditions risks. Thus, enterprise managers need more training on national laws and related cultures. They must also strengthen their capacity to cope with natural disasters such as rain and droughts. Employees should enhance their abilities to manage emergencies. Moreover, it is suggested that investors prepare scientific and realistic risk prevention plans and strategies to ensure energy infrastructure projects’ efficiency and overall goals.
3.3.1. Sample pretreatment The risk factor occurrence probability samples that were not collected for five years are completed by applying equation (9). Using the subsequent 780 samples, the risk data are normalized by applying equation (10) to solve the problem of different dimensional data failing to be analyzed (see Tables 2 and 3). 3.3.2. Coding Based on the normalization results in Table 3, we randomly select a set of initial weights and thresholds from the [0,1] interval among the 11 types of risks and 39 risk factors, using the decimal method. Code programming is performed in MATLAB 2016a using equations (2)–(4), where the threshold is controlled as α = ± 0.1 and the floating coefficient β = 0.01. The two groups with the highest risk and the two groups with the lowest risk are selected for weight debugging (see Table 4). 3.3.3. Training samples Fig. 4 presents a comparison of risk training samples using the BP (back propagation) model and the IAFSA–CMAC. The model input is 11 Table 2 Risk factors of infrastructure projects at the Barapukuria Coal Mine. Group number
Risk factors y1
y2
y3
x1
x2
x3
x4
x5
x6
x7
x8
x9
0.32 0.20 0.58 0.00 1.00
0.12 0.22 1.00 0.41 0.00
0.05 1.00 0.76 0.43 0.00
0.11 0.45 0.69 0.00 1.00
0.00 0.59 0.14 1.00 0.66
0.73 0.63 0.86 0.00 1.00
0.80 0.00 1.00 0.17 0.44
0.21 0.67 0.82 0.00 1.00
0.82 0.46 0.70 1.00 0.00
x10
x11
y4 x12
x13
x14
x15
y5 x16
x17
yn xn
0.00 0.28 0.55 0.48 1.00
0.00 0.55 0.20 0.73 1.00
0.00 0.00 0.00 0.00 1.00
1.00 0.52 0.00 0.98 0.67
0.21 0.37 0.63 0.00 1.00
0.57 0.28 0.13 1.00 0.00
0.00 0.39 1.40 1.00 0.00
0.58 1.00 0.00 0.06 0.46
… … … … …
4. Discussion 1 2 3 4 5 Group number
1 2 3 4 5
(i) After we applied an IAFSA–CAMC risk prediction model to the study of risk level prediction for the Barapukuria Coal Mine, the results show risk occurrence probabilities from 2018 to 2023 of 0.3543–0.4628 (low), 0.2294–0.3633 (medium–low), 0.3714–0.4553 (medium–low), 0.4111–0.5371 (medium), 0.4273–0.5311 (medium), and 0.3682–0.5017 (medium–low) respectively. The results show that a slightly higher risk occurrence probability exists during the construction of a coal mine enterprise (in this case, the Barapukuria Coal Mine). In the future, the likely risks for the Barapukuria Coal Mine are natural disaster, social stability, cultural and religious difference, investment, non-traditional security, and outsourcing conditions. (ii) Compared with other risk prediction methods, the IAFSA–CMAC
Risk factors
Data source: Investment Feasibility Analysis of the Barapukuria Coal Mine in Bangladesh. 115
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Table 4 Weight calculation by artificial fish swarm algorithm. Risk-weight range
X1
X2
X3
X4
X5
X6
Maximum Minimum Risk-weight range Maximum Minimum
0.0913 0.0612 X7 0.0870 0.0583
0.0928 0.0622 X8 0.0750 0.0503
0.1177 0.0789 X9 0.1231 0.0825
0.0742 0.0497 X10 0.0771 0.0517
0.0891 0.0597 X11 0.0813 0.0545
0.0914 0.0612
Comparation
X1: Natural condition risk, X2: Social risk, X3: World situation changing risk, X4: Political risk, X5: Market risk, X6: Funding risk, X7: Cultural and religious risk, X8: Technical risk, X9: Safety risk, X10: External conditions risk, X11: Organizational management risk.
Fig. 4. Error values comparison of risk training samples using coupling algorithms of IAFSA–CMAC and the standard BP algorithm.
Table 5 Error comparisons of the verification results. Sample number
Output value of expectation
Synthetic risk occurrence probability of 2013–2016: prediction results by traditional method
5 10 15 20
0.4778 0.4400 0.4864 0.3458
0.4364 0.4750 0.5133 0.3624
Coupling algorithm prediction of IAFSA and CMAC network:
Error (%)
verification results 0.3813–0.4724 0.4071–0.4931 0.3613–0.5330 0.3471–0.4111
−0.0551–0.0360 −0.0679–0.0181 −1.2700–1.2600 0.0247–0.0487
predicted effectiveness. Moreover, when analyzing a prediction by this method, data are probably minimized unlimitedly. Consequently, large amounts of resources must be invested to achieve valuable information analysis. (iv) This study shows that the probability of risk occurrence is a probability interval with a dynamic risk combination influenced by time variation. However, a maximum value superposition of a different probability of risk occurrence was proved to be irrelevant. Considering the significant investment and complicated types of risks involved in large-scale energy projects from Belt and Road Initiative countries, it is obviously not rational or scientific to explain and assess the probability of risk occurrence through the value obtained by the former instead of the latter method. Thus, an urgent need exists to explore the probability dynamic variation rule within a different risk prediction range as a scientific problem.
has many advantages. In particular, it resolves numerous limitations of other methods. For example, the analytic hierarchy process (AHP) can achieve the layered calculation of data and improve the accuracy of risk assessment and prediction by integrating other methods. However, the result obtained by AHP normally establishes specific probability values, rather than an interval. In addition, because data are easily lost during the process of AHP calculation, applying this method to projects under construction is not recommended. Further, the vague prediction method can improve vagueness in risk identification but neglects risk data effectiveness when a project is under construction. Other methods such as grey system prediction and causal model prediction are poor in terms of adaptive ability. (iii) The IAFSA–CMAC can identify data from the first layer to calculate risk cases and risk probability occurrence. It summarizes different risk cases' frequency of occurrence in various years, making automatic calculations and adjusting cases' weight to obtain different types of risk ratios with the biggest or smallest weights. Further maximum and minimum possibilities of different types of risks can also be precisely represented. Thus, in this context, the IAFSA–CMAC offers two advantages: obvious compatibility and
5. Conclusion and recommendations This study researches the problem of a risk prediction value that is less than the actual value in coal enterprise infrastructure projects of countries along the Belt and Road. We analyzed various influential risk 116
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Fig. 5. Risk probability prediction results with coupling algorithms of IAFSA–CMAC.
References
factors, taking the example of the construction of the Barapukuria Coal Mine, and established a prediction indicator system that applies to the probability of risk occurrence for energy infrastructure projects. However, projects' risk assessments are greatly affected by time and the conductivity coefficient, which is an existing defect. Combining the IAFSA algorithm with the CMAC neural network model, a parallel coupling algorithm was introduced to calculate a comprehensive probability of risk occurrence and correct the defect. The problem of the mismatch of risk prediction value and the actual value has also been solved. Hence, we tried to divide the probability interval of risks that occurred at the Barapukuria Coal Mine from 2017 to 2023. The calculated results showed that the delayed relationship between predicted risk and actual risk can be evaluated quantitatively. This finding means that the IAFSA–CMAC parallel coupling algorithm is capable of predicting precisely the probability of risk occurrence for coal enterprise infrastructure projects of countries along the Belt and Road. Such a result has significant scientific reference value to future risk management and control in coal enterprises. Thus, some suggestions are now given that are related to risk types with a maximum probability of occurrence in the coal enterprises of Belt and Road Initiative countries along the Belt and Road. We expect that enterprises can improve their understanding of national laws and of culture, and strengthen their capacities to resist natural disasters such as rain and droughts. It is also necessary to enhance their employees' ability to manage emergencies. In particular, unwise investment may be avoided if investors and managers recognize opportunities and risks. They are recommended to organize resources carefully and prepare risk controls and strategies in advance to ensure energy infrastructure projects’ successful implementation in terms of efficiency and overall goals.
Boyles, A.L., Blain, R.B., Rochester, J.R., Avanasi, R., Goldhaber, S.B., Mccomb, S., Holmgren, S.D., Masten, S.A., Thayer, K.A., 2017. Systematic review of community health impacts of mountaintop removal mining. Environ. Int. 107, 163–172. Fang, Y.N., 2016. Analysis on risk of Chinese enterprises' overseas direct investment in infrastructure construction of the countries along “one belt and one road”. J. Xian Univ. Finance Econ. 29, 67–72. Gong, B., Zeng, F.Y., 2016. A research on the improvement of artificial fish algorithm and its optimization on BP neural network. J. Hunan Univ. Sci. Technol. (Nat. Sci. Ed.) 31, 86–90 (In Chinese). Hao, Q., Zuo, Y., Lintao, L.I., Chen, W., Junjie, Y.I., Lei, W.U., 2017. The distribution of petroleum resources and characteristics of main petroliferous basins along the Silk road economic belt and the 21st-century Maritime Silk road. Acta Geol. Sin. (English Edition) 91, 1457–1486. He, Q., Hu, X., Ren, H., Zhang, H., 2015. A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem. ISA Trans. 59, 105–113. Howladar, M.F., Deb, P.K., Muzemder, A.T.M.S.H., Ahmed, M., 2014. Evaluation of water resources around Barapukuria coal mine industrial area, Dinajpur, Bangladesh. Appl. Water Sci. 4, 203–222. Lai, L., Guo, K., 2017. The performance of one belt and one road exchange rate: based on improved singular spectrum analysis. Phys. Stat. Mech. Appl. 483, 299–308. Lin, S., Li, C., Xu, F., Liu, D., Liu, J., 2018. Risk identification and analysis for new energy power system in China based on D numbers and decision-making trial and evaluation laboratory (DEMATEL). J. Clean. Prod. 180, 81–96. Ma, Y., University, S., 2015. The risk control of constructing China's “belt and road” initiative. China Rev. Polit. Econ. 6, 189–203. Monaghan, A.A., 2017. Unconventional energy resources in a crowded subsurface: reducing uncertainty and developing a separation zone concept for resource estimation and deep 3D subsurface planning using legacy mining data. Sci. Total Environ. 45, 601–602. Moreno, V.C., Cozzani, V., 2018. Integrated hazard identification within the risk management of industrial biological processes. Saf. Sci. 103, 340–351. Tavakolan, M., Etemadinia, H., Tavakolan, M., Etemadinia, H., 2017. Fuzzy weighted interpretive structural modeling: improved method for identification of risk interactions in construction projects. J. Construct. Eng. Manag. 143, 01–14. Tsai, C.H., Yeh, M.F., 2009. Application of CMAC neural network to the control of induction motor drives. Appl. Soft Comput. 9, 1187–1196. Wei, L.X., Zhang, J.L., Liu, Q.S., 2014. Optimization of neural network based on improved fish algorithm. Contr. Eng. China 21, 84–87. Xu, L.J., Fan, X.C., Wang, W.Q., Xu, L., Duan, Y.L., Shi, R.J., 2017. Renewable and sustainable energy of Xinjiang and development strategy of node areas in the “Silk Road Economic Belt”. Renew. Sustain. Energy Rev. 79, 274–285. Zhang, Z., Wang, K., Zhu, L., Wang, Y., 2017. A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Syst. Appl. 86, 165–176.
Acknowledgments This work was supported by the National Natural Science Foundation of China (grant numbers 71273208 , 71271169 and 51874237).
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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Risk probability predictions for coal enterprise infrastructure projects in countries along the Belt and Road Initiative
T
Hongxia Lia, Yixin Huangb,∗, Shuicheng Tianc a
School of Management, Xi'an University of Science and Technology, Xi'an, 710054, China School of Energy, Xi'an University of Science and Technology, Xi'an, 710054, China c School of Safety, Xi'an University of Science and Technology, Xi'an, 710054, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Risk prediction Improved artificial fish swarm algorithm (IAFSA) Cerebellar model articulation controller (CMAC) Barapukuria coal mine The Belt and Road initiative
The Belt and Road Initiative has significantly promoted the construction and development of coal enterprises along the Belt and Road through the building of a new platform that encourages global economic cooperation. This study examines problems caused by the uncertainty of risk occurrence during the process of coal enterprise construction as part of the Belt and Road Initiative. Consequently, the study identifies and classifies various risk factors. In total, 11 types of risks and 39 risk factors involved in the construction process are summarized. These include natural, cultural, religious, marketing, and outsourcing-related risks. A prediction indicator system was also established to apply to the risk occurrence probability related to coal mine construction along the Belt and Road. In this regard, the study embedded an improved artificial fish swarm algorithm (IAFSA) into a cerebellar model articulation controller (CMAC) neural network model and collected risk probability details for the construction of the Barapukuria Coal Mine in Bangladesh from 2013 to 2018 as sample data. By researching the model and sample, this study obtained various risk occurrence probability intervals. Moreover, diversified risk probabilities were verified and predicted. Finally, this study empirically proves that an IAFSA–CMAC parallel coupling algorithm is able to achieve precise predictions about risks. This finding has great significance for risk management and control in the coal enterprises of countries along the Belt and Road.
1. Introduction The initiative to jointly build the Silk Road Economic Belt and the Twenty-First Century Maritime Silk Road (namely, the Belt and Road Initiative) has great significance for the promotion and realization of a community of common destiny for coal enterprises from different countries. Countries along the Belt and Road have abundant coal resources. Indeed, 15 countries have more than 10 billion tons of coal reserves, accounting for 46.9% of the world's total. Owing to the infrastructure project's characteristics, which are non-recyclable, irreversible, and long term (Boyles et al., 2017; Monaghan, 2017), complex and frequent risks may occur during implementation. These risks may cause immeasurable losses for coal mining enterprises. This problem is common among coal mining enterprises, especially those in the Belt and Road Initiative countries, when infrastructure projects are constructed (Fang, 2016; Hao et al., 2017; Ma and University, 2015; and Xu et al., 2017). For example, in 2013, the Dahaize Mining Industry in Shaanxi, China, was forced to cease production because of frequent safety violations and accept inspections. Similarly, in 2015, some coal
∗
mining projects in Pakistan were shelved owing to disputes related to energy ownership. This current study researches the minimization of the risk occurrence probability in coal mining in the countries along the Belt and Road. Other experts (Lin et al., 2018; Moreno and Cozzani, 2018; Tavakolan et al., 2017) proposed a comprehensive approach to risk identification in accordance with the evaluation of occurrence probability. Their studies contributed to a control mechanism of shared regional capital cooperation and targeted risks, thereby providing effective support to reduce risk occurrence probability. Meanwhile, many other experts (Hao et al., 2017; Lai and Guo, 2017) have conducted useful research on risk assessment and prediction for engineering projects in the Belt and Road Initiative countries, thereby giving theoretical support for risk management and the control of energy enterprises' infrastructure projects. However, following research under the special circumstances of the Belt and Road Initiative on numerous relevant countries and cities, it is clear that the speed of risk changes is much higher than that of risk evaluation. This situation has resulted in less predicted data of the risk frequency of occurrence than required. Therefore, an easier algorithm for risk probability prediction is urgently
Corresponding author. E-mail addresses: [email protected] (H. Li), [email protected] (Y. Huang), [email protected] (S. Tian).
https://doi.org/10.1016/j.ergon.2018.10.006 Received 3 May 2018; Received in revised form 22 August 2018; Accepted 31 October 2018 0169-8141/ © 2018 Elsevier B.V. All rights reserved.
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north, offering direct services to the capital, Dhaka. The region is part of the Ganges alluvial plain, which consists of sediments deposited by the Ganges and Jamuna rivers. The terrain of the area is flat, with an elevation of +29 m–32 m. The north is slightly higher than the south, but the gradient is very low.
needed. Such an algorithm should also have faster convergence and excellent generalization ability with the aim of improving the theoretical system for risk management of infrastructure projects in countries along the Belt and Road. A great deal of research exists on the artificial fish swarm algorithm (AFSA) and the cerebellar model articulation controller (CMAC) neural network. Zhang et al. (2017) presented a Pareto improved AFSA (IAFSA) to solve the multi-objective disassembly line-balancing problem with fuzzy disassembly times. He et al. (2015) proposed a novel AFSA for solving large-scale reliability redundancy allocation problems (RAPs). Moreover, because the CMAC is a neural network that features partial approximation, speediness, and briefness, it enables the study of random multidimensional nonlinear fitting. Neurophysiology based on CMAC was proposed by J. S. Albus in 1975 and was later applied to various areas. Tsai and Yeh (2009) developed a CMAC neural network based on a control system for a speed-sensorless induction motor. The current study considers the neural network theory and the adaptive functions and faster convergence of related models, to develop a more suitable methodology for mining risk assessment. The structure related to the theory is proposed as follows.
2.2. Framework The framework of risk prediction for the Barapukuria Coal Mine is shown in Fig. 2 in five parts as follows: 1) risk identification; 2) data collection; 3) data preprocessing; 4) model building; and 5) risk prediction. First, according to related indicators in the BP-Energy Statistics Yearbook (2017), we analyze and review the literature by the expert scoring method. We then determine the final risk index by the fuzzy clustering method and employ it to consider risk influence factors. With the latter, we construct a new standard for our research, referring to the risk measuring standards specified in Coal Mine Project Risk Investment Guidance (2017). Second, the data, derived from the site by workers and managers, were collected. The workers and managers recorded the accident types and the numbers of each accident. Then, they divided the numbers according to the days in a year to obtain a decimal figure, which is the frequency of risk. Third, we construct a risk prediction model featuring timeliness and adaptability. We also consider the particularity of risk prediction in countries along the Belt and Road. We conduct normalized analysis after a pre-process to fill in the missing data by introducing linear regression and the collection of multi-risk isomerous data (structured and unstructured data). Based on the foregoing, and combined with the neural network theory and an IAFSA, a traditional CMAC neural network theory model was improved into a new CMAC model suitable for the risk prediction of projects along the Belt and Road.
- In the section 2, we discuss recent research to present the background and progress of mining infrastructure risk probability research. The current problem is also outlined. - Section 3 combines a literature review with a description of the understanding of risk characteristics and how they apply in different areas. We identify the most important influential and effective factors and build an index system based on factor identification. - In section 4, the mining infrastructure characteristics are combined with neural network theory to build the risk probability model in terms of the Barapukuria Coal Mine scenario. - In accordance with our research results, conclusions and improvement measures are given in section 5.
2.3. Improved methods
2. Materials and methods
2.3.1. Improving AFSA Generally, there are two approaches to weight adjustment for CMAC: the learning approach, which used the Jacobi iterative method in linear algebra, and the rotation approach, which uses the Gauss–Seidel iterative method. However, the weight calculated by either of these methods is a value instead of a range of values. Thus, by applying the clustering characteristics of artificial fish swarms, we calculated and predicted the interval range of risk probability and avoided risk fluctuations probably caused by time or conduction coefficients that consider the specificity of risk probability. We then optimize the artificial fish to achieve global optimization (Gong and Zeng, 2016; Wei et al., 2014). The IAFSA was used to adjust the weight size and calculate the weight range for each risk. The state of an individual artificial fish, Is(t), at time t is defined as
2.1. Study area Many cities and countries are involved in the Belt and Road Initiative. Unfortunately, the current difficult hydrogeological conditions, high ground temperatures, and complex cultural environments are rarely identified in most risk assessment reports. The result is undesirable factors in mining. Bangladesh is located along the Belt and Road. There is a mine in the country named the Barapukuria Coal Mine. Its advantages include convenient transportation, thick coal seams, abundant reserves, goodquality coal, a simple geological structure, and low biogas volume. Thus, the Barapukuria Coal Mine is a typical energy infrastructure project along the Belt and Road. It is also the first large-scale modernized mine developed and constructed in Bangladesh. Unexpectedly, its risk prediction has not been discussed in detail in studies about the mine's risk assessment. Thus, it is useful to take the Barapukuria Coal Mine (see Fig. 1) as an illustrative example. The productive capacity of Barapukuria Coal mine is 1.0 Mt/a. It is located between the latitudes of 25°31′ and 25°34′ N and 88°57′ and 88°59’ E in the northwest of Bangladesh, near Parbatipur in Dinajpur District. Within the surrounding area, transportation is quite convenient. The region is crossed by a broad-gauge railway from south to north on the western boundary, acting as an interchange between the north–south broad-gauge and the east–west meter-gauge railways. Approximately 5 km south of the mine, there is an east–west highway that eastwardly connects to another national highway. The latter goes 5 km northwest to Dinajpur, the provincial capital, and 75 km south to a northwestern city called Bogra along that highway. Moreover, located 70 km east of the mine, the Jamuna River stretches from north to south, reaching many domestic ports. Saidpur Airport is 10 km toward the
Is (t ) = {I1s (t ), I2s (t ), … Iωsi (t ) }, s = 1,2, …, AF _number
(1)
where AF_number represents the scale of the artificial fish swarm, and w represents the weight range. The CMAC has a hidden layer network; as such, the initial setting input layer X set has x inputs, the number of dimensions w is hn, and the number of output layer Y sets is m. The formula is w = h1(m+1) + h2(h1+1) + n(h2+1)
(2)
To ensure that the weights debugged by the AFSA match the generalization ability of the CMAC model, the AF values should be ordered from bottom to top in accordance with the weight distribution of the CMAC model. The first few sets of data with the highest risk activity are selected first; then the sets of data with the lowest risk activity. Thus, after several rounds of rotation, the state of the sth AF can be indicated by a value or a range of values as follows: Is(t)(i = 1, 2, …, w), 111
(3)
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Fig. 1. The location and geologic and tectonic setting of the Barapukuria Coal Mine (modified from Howladar et al., 2014).
Fig. 2. Risk prediction network framework model of CMAC.
process, it is impossible for all the risks of energy enterprise infrastructure projects in the Belt and Road countries to be triggered at once. Thus, an element, xi, inputted into space X simultaneously activates all yn types of risk in the real space, A. This situation causes the superposition value of yn risk weights to be 1. Other risks that do not occur are zero. Here, n is C, the generalization ability of the CMAC network. The explanation for each layer is as follows. The first layer is the input layer. Factors in the “virtual” space, X, are mapped according to discrete points in the real space, A. Then, X = {x1, x2, …, xn} is quantified and performed for data preprocessing. If a component of the risk factor occurrence probability of each risk can be quantified as a value, q, the set of components of the n risk factors can become a risk state-space, qn. As such, the quantification formula of the specific risk occurrence probability is
Where, Xs(t) indicates the state of the AF. The difference in generalization ability controls the size of the AF, while fluctuations of the AF influence wn. The variable wn is ωi in the CMAC model. 2.3.2. The IAFSA embedded in the CMAC model The CMAC network model is considered an “associative controller” network model, which is similar to a neural cerebellar cortical structure. Fig. 2 shows the network structure of the two-dimensional input/ one-dimensional output CMAC risk probability prediction model constructed in this study. The area labeled X in Fig. 2 represents the occurrence probability of a set of risk factors with an n-dimensional input, which is the “virtual” space of the input variable. The real space, A, represents the “storage units” with m types of risk. X={x1, x2, …, xn}T indicates all n-dimensional input components in the area X. It also represents the characteristic components of the risk and is referred to as the influencing factors that trigger risk. Y={y1, y2, …, yn}T indicates the eigenvector corresponding to the output variable. In the actual
Xp = {x1, x2, …, xn}T, n = 1, 2, …, n, p
(4)
Where X represents a set of risk factors and xn represents the number of 112
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coal market demand, mining's competitiveness, and price changes for coal (and coal chemical products).
components in the eigenvector. The second layer is the middle layer. To perform the unique mapping of the virtual space, Xp, onto the real space Yp, the number of “storage units” in the real space, Yp, must be greater than or equal to the number of states in the “virtual” space, Xp. Thus, the formula is m ≥ qn
3.1.3. Technical risk Normally, technical risk relates to construction's particularity, technological advancement, and equipment reliability. Thus, multiplefactor analysis is required for specific technical risk problems. For example, some factors involved in technological advancement, applicability, reliability, and reasonability must be considered during construction.
(5)
Where m represents the number of storage unit states in Yp and qn represents n numbers of characteristic components. Thus, the input relationship of the second layer is as follows. Input relationship: Xp={x1, x2, …, xn}T, n = 1, 2, …, n
3.1.4. Geological and technical mining conditions risk Contrary to expectations, diverse geological structures and engineering conditions may result in risks related to geological and technical mining conditions. The threat of the risks may lead to increased investment or construction extensions. The risk factors include hydrogeological engineering conditions, coal seam conditions, spontaneous combustion, and gas.
(6)
Output relationship Y ={y1, y2, …, ypn}T, p = 1, 2, …, qn; n = 1, 2, …, n p
(7)
p
Where Y represents a set of risk factors, yn represents the number of components in the eigenvector, and p represents the number of statespaces in the set of characteristic components. It is then possible to use hash coding to map the real space, A, which has qn storage unit addresses, onto a compressed space, Yp, with physical addresses. The third layer is the output layer (risk probability prediction layer). Through the assessment of risk as a combination of characteristic components in the first layer, and the unique mapping of the “virtual” space, Xp, onto the real space, Yp, the CMAC network risk output is the weight value of the risk storage that has gone through learning and accumulated to 1, achieving a prediction for risk probability (extent of loss). Thus, the network output of a certain input, Xi, is
P (Y p) = F (x ) =
∑
wi
i=Y ∗
3.1.5. Economic foundation risk Economic foundation risk mainly includes the exchange rate, the interest rate, and funding sources. Because funds for a mining construction project are always in demand, a funding shortage or interruption may lead to a project's delay or even termination. Interest rate and exchange rate changes may also have a significant impact on financing costs. For example, if interest rates and exchange rates rise, financing costs will rise simultaneously, resulting in increased costs and reduced profit margins. 3.1.6. Organizational management risk Irrationalities may occur during planning, construction, and coordination, and in human resources management, thereby affecting project implementation schedules and operational efficiency. These irrationalities refer to organizational management risk, which includes unreasonable organizational structure and incomplete regulations and supervision.
(8)
where Y* represents the number of risks that may occur and ωi represents the corresponding weight of the risk. 3. Results analysis
3.1.7. Safety risk A safety risk is a problem that has an impact on a project's sustainable development. Such risk is classified into the traditional safety risk of an accident and non-traditional risk. The former includes equipment failure, serious injury, and mortality. The latter includes riots, armed conflicts, and wars.
3.1. Risk classification Relative studies have combined with the reasons for energy enterprises’ risks in the Belt and Road Initiative area and considered research reports. Fuzzy clustering is used to classify risks into 11 categories and identify 39 risk factors that trigger various types of risks. Fig. 3 shows that as a risk identification system, the data visualization that is used to perform risk identification for energy enterprise infrastructure projects along the Belt and Road. Mining risks normally occur during the stage of early feasibility studies, project construction, and operations. Several foreign projects’ published reports and research results have shown that, during the construction and operation stages, there is little probability of risk; risk is more likely to be seen at the stage of feasibility studies. In addition, other types of risks vary at different phases. Thus, we conclude that specific kinds of risks exist as follows.
3.1.8. External conditions risk External conditions risk directly influences coal mines’ efficiency at the operational stage. The risks include carriage conditions, transportation, water and power supply, and public relations. 3.1.9. Social stability risk Social stability risk refers to a project in the process of construction or already running that may be affected by its social environment. In addition to conventional factors, public security and riots are included in social stability risk. Table 1 shows the risk classification in a large mining industry. The Belt and Road Initiative area, as outlined earlier, uses the risk identification of energy enterprise infrastructure projects and the risk classification standards in The Guideline of Construction Project Feasibility Study. The frequencies of risk and its factors are divided into five levels, which is the theoretical basis for data samples and risk probability prediction.
3.1.1. Policy risk Policy risk refers to the impact of national macroeconomic policy on project construction. Such risk includes changes under existing political and economic conditions and energy policy adjustments. Risk may also be affected by compliance with relevant policies. 3.1.2. Marketing risk Marketing risk is among the major risks often encountered by competitive projects. It is mainly manifested as poor sales of coal products, product prices being in the doldrums, and failure to achieve production and sales revenue goals. The main influential factors are
3.2. Data normalization 3.2.1. Missing data Data may be missing during the collection process, which can be 113
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Fig. 3. Coal enterprises' infrastructure projects involved in 11 categories and 39 risk factors.
respectively. We also assume that the sample data of risk eigenvector Xi is classified in level k, where the upper and lower limits are (A)ik and Bik yik respectively. Finally, we assume Mik is the corresponding classification level of the range of values between the upper and lower limits of the risk factor's characteristic component and risk probability. Then, the stochastic simulation formula for the occurrence probability of that risk factor is
Table 1 Risk occurrence frequency level (unit: %). Risk probability level
Low
Medium–low
Medium
Medium–high
High
Occurrence frequency
0–20
20–40
40–60
60–80
≥80
replaced by interpolation generation. Taking into account the classification levels of risk occurrence frequency and risk factors, this method employs the stochastic distribution of the risk probability of an energy infrastructure project in an area selected for prediction. Such samples and their classification levels are used to construct the model's initial sequence. We assume the risk characteristic component (risk factor), xi, is classified in level k, where the upper and lower limits are aik and bik
P(x, y)=(xkij, yki),
(9)
Where P represents the risk occurrence frequency, j represents the sample sequence capacity generated by a certain classification level, i represents certain risk, and k represents the number of classification levels. According to the model above, the risk occurrence frequency of classification level k produces nk groups of data samples. 114
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3.2.2. Normalization A total of 39 risk factors and 11 risk indicators are in the prediction of risk probability for energy infrastructure projects. Because a big difference exists between each indicator's units and dimensions, all the risk indicators are normalized when calculated in order to minimize the impact and magnitude of such differences. The normalization equation is X*i
= Xi / Xmax × 100%,
Table 3 Partial normalization for risk factors’ uniform values at the Barapukuria Coal Mine. Group number
1 2 3 4 5
(10)
X*i
represents the normalized risk indicator, Xi represents the Where normalized risk factor value, and X-max represents the maximum risk factor indicator in the sample.
Risk factors' uniform values y1
y2
y3
y4
y5
y6
y7
y8
yn
0.28 0.00 0.54 0.32 1.00
0.45 0.64 1.00 0.22 0.00
0.37 0.00 0.10 0.32 1.00
0.00 1.00 0.62 0.36 0.62
1.00 0.09 0.62 0.21 0.00
0.00 0.55 0.26 1.00 0.73
0.73 1.00 0.14 0.00 0.29
0.61 1.00 0.03 0.00 0.79
… … … … …
Data source: Investment Feasibility Analysis of the Barapukuria Coal Mine in Bangladesh.
3.3. Risk prediction and data analysis types of risks and 39 risk factors, while the output is the occurrence probability calculated for each risk factor. Using the generalized parameter C = 11 and the occurrence probability as the output, the five groups with the highest risk and the five groups with the lowest risk are employed as training samples. The remaining samples are used for prediction.
Based on the classification levels of risk and risk factors, the occurrence frequency of different risks from 2012 to 2017 is extracted from the Manual of Infrastructure Construction (Phase III) of the Barapukuria Coal Mine in Bangladesh. A risk prediction is performed for the Barapukuria Coal Mine, providing it with a new reference for future development and operations.
3.3.4. Verification The interval of the solution's total risk occurrence probability is compared with the actual total risk probability in 2016. The relative error can then be calculated. This approach enables the volatility difference of risk occurrence to be a floating coefficient in future predictions (see Table 5). Based on this risk prediction model, the risk occurrence probability of mining infrastructure projects in the Barapukuria Coal Mine from 2018 to 2023 is predicted using the sample data in the remaining 10 groups of risk and risk factors, thereby providing a reference for decision-makers. The actual evaluation and prediction results are shown in Fig. 5. The results in Fig. 5 show that the risk occurrence probabilities for 2018–2023 are 0.3543–0.4628, 0.2937–0.3633, 0.3714–0.4553, 0.4111–0.5371, 0.4273–0.5311, and 0.3682–0.5017 respectively. These are considered low risk, medium–low risk, medium–low risk, medium risk, medium risk, and medium–low risk levels respectively. The six groups of data highlight that there may be a higher risk probability for the coal enterprise infrastructure in Bangladesh within the next six years. The factors include natural disaster risk, social stability risk, cultural and religious difference risk, investment risk, non-traditional security risk, and outsourcing conditions risks. Thus, enterprise managers need more training on national laws and related cultures. They must also strengthen their capacity to cope with natural disasters such as rain and droughts. Employees should enhance their abilities to manage emergencies. Moreover, it is suggested that investors prepare scientific and realistic risk prevention plans and strategies to ensure energy infrastructure projects’ efficiency and overall goals.
3.3.1. Sample pretreatment The risk factor occurrence probability samples that were not collected for five years are completed by applying equation (9). Using the subsequent 780 samples, the risk data are normalized by applying equation (10) to solve the problem of different dimensional data failing to be analyzed (see Tables 2 and 3). 3.3.2. Coding Based on the normalization results in Table 3, we randomly select a set of initial weights and thresholds from the [0,1] interval among the 11 types of risks and 39 risk factors, using the decimal method. Code programming is performed in MATLAB 2016a using equations (2)–(4), where the threshold is controlled as α = ± 0.1 and the floating coefficient β = 0.01. The two groups with the highest risk and the two groups with the lowest risk are selected for weight debugging (see Table 4). 3.3.3. Training samples Fig. 4 presents a comparison of risk training samples using the BP (back propagation) model and the IAFSA–CMAC. The model input is 11 Table 2 Risk factors of infrastructure projects at the Barapukuria Coal Mine. Group number
Risk factors y1
y2
y3
x1
x2
x3
x4
x5
x6
x7
x8
x9
0.32 0.20 0.58 0.00 1.00
0.12 0.22 1.00 0.41 0.00
0.05 1.00 0.76 0.43 0.00
0.11 0.45 0.69 0.00 1.00
0.00 0.59 0.14 1.00 0.66
0.73 0.63 0.86 0.00 1.00
0.80 0.00 1.00 0.17 0.44
0.21 0.67 0.82 0.00 1.00
0.82 0.46 0.70 1.00 0.00
x10
x11
y4 x12
x13
x14
x15
y5 x16
x17
yn xn
0.00 0.28 0.55 0.48 1.00
0.00 0.55 0.20 0.73 1.00
0.00 0.00 0.00 0.00 1.00
1.00 0.52 0.00 0.98 0.67
0.21 0.37 0.63 0.00 1.00
0.57 0.28 0.13 1.00 0.00
0.00 0.39 1.40 1.00 0.00
0.58 1.00 0.00 0.06 0.46
… … … … …
4. Discussion 1 2 3 4 5 Group number
1 2 3 4 5
(i) After we applied an IAFSA–CAMC risk prediction model to the study of risk level prediction for the Barapukuria Coal Mine, the results show risk occurrence probabilities from 2018 to 2023 of 0.3543–0.4628 (low), 0.2294–0.3633 (medium–low), 0.3714–0.4553 (medium–low), 0.4111–0.5371 (medium), 0.4273–0.5311 (medium), and 0.3682–0.5017 (medium–low) respectively. The results show that a slightly higher risk occurrence probability exists during the construction of a coal mine enterprise (in this case, the Barapukuria Coal Mine). In the future, the likely risks for the Barapukuria Coal Mine are natural disaster, social stability, cultural and religious difference, investment, non-traditional security, and outsourcing conditions. (ii) Compared with other risk prediction methods, the IAFSA–CMAC
Risk factors
Data source: Investment Feasibility Analysis of the Barapukuria Coal Mine in Bangladesh. 115
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Table 4 Weight calculation by artificial fish swarm algorithm. Risk-weight range
X1
X2
X3
X4
X5
X6
Maximum Minimum Risk-weight range Maximum Minimum
0.0913 0.0612 X7 0.0870 0.0583
0.0928 0.0622 X8 0.0750 0.0503
0.1177 0.0789 X9 0.1231 0.0825
0.0742 0.0497 X10 0.0771 0.0517
0.0891 0.0597 X11 0.0813 0.0545
0.0914 0.0612
Comparation
X1: Natural condition risk, X2: Social risk, X3: World situation changing risk, X4: Political risk, X5: Market risk, X6: Funding risk, X7: Cultural and religious risk, X8: Technical risk, X9: Safety risk, X10: External conditions risk, X11: Organizational management risk.
Fig. 4. Error values comparison of risk training samples using coupling algorithms of IAFSA–CMAC and the standard BP algorithm.
Table 5 Error comparisons of the verification results. Sample number
Output value of expectation
Synthetic risk occurrence probability of 2013–2016: prediction results by traditional method
5 10 15 20
0.4778 0.4400 0.4864 0.3458
0.4364 0.4750 0.5133 0.3624
Coupling algorithm prediction of IAFSA and CMAC network:
Error (%)
verification results 0.3813–0.4724 0.4071–0.4931 0.3613–0.5330 0.3471–0.4111
−0.0551–0.0360 −0.0679–0.0181 −1.2700–1.2600 0.0247–0.0487
predicted effectiveness. Moreover, when analyzing a prediction by this method, data are probably minimized unlimitedly. Consequently, large amounts of resources must be invested to achieve valuable information analysis. (iv) This study shows that the probability of risk occurrence is a probability interval with a dynamic risk combination influenced by time variation. However, a maximum value superposition of a different probability of risk occurrence was proved to be irrelevant. Considering the significant investment and complicated types of risks involved in large-scale energy projects from Belt and Road Initiative countries, it is obviously not rational or scientific to explain and assess the probability of risk occurrence through the value obtained by the former instead of the latter method. Thus, an urgent need exists to explore the probability dynamic variation rule within a different risk prediction range as a scientific problem.
has many advantages. In particular, it resolves numerous limitations of other methods. For example, the analytic hierarchy process (AHP) can achieve the layered calculation of data and improve the accuracy of risk assessment and prediction by integrating other methods. However, the result obtained by AHP normally establishes specific probability values, rather than an interval. In addition, because data are easily lost during the process of AHP calculation, applying this method to projects under construction is not recommended. Further, the vague prediction method can improve vagueness in risk identification but neglects risk data effectiveness when a project is under construction. Other methods such as grey system prediction and causal model prediction are poor in terms of adaptive ability. (iii) The IAFSA–CMAC can identify data from the first layer to calculate risk cases and risk probability occurrence. It summarizes different risk cases' frequency of occurrence in various years, making automatic calculations and adjusting cases' weight to obtain different types of risk ratios with the biggest or smallest weights. Further maximum and minimum possibilities of different types of risks can also be precisely represented. Thus, in this context, the IAFSA–CMAC offers two advantages: obvious compatibility and
5. Conclusion and recommendations This study researches the problem of a risk prediction value that is less than the actual value in coal enterprise infrastructure projects of countries along the Belt and Road. We analyzed various influential risk 116
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H. Li et al.
Fig. 5. Risk probability prediction results with coupling algorithms of IAFSA–CMAC.
References
factors, taking the example of the construction of the Barapukuria Coal Mine, and established a prediction indicator system that applies to the probability of risk occurrence for energy infrastructure projects. However, projects' risk assessments are greatly affected by time and the conductivity coefficient, which is an existing defect. Combining the IAFSA algorithm with the CMAC neural network model, a parallel coupling algorithm was introduced to calculate a comprehensive probability of risk occurrence and correct the defect. The problem of the mismatch of risk prediction value and the actual value has also been solved. Hence, we tried to divide the probability interval of risks that occurred at the Barapukuria Coal Mine from 2017 to 2023. The calculated results showed that the delayed relationship between predicted risk and actual risk can be evaluated quantitatively. This finding means that the IAFSA–CMAC parallel coupling algorithm is capable of predicting precisely the probability of risk occurrence for coal enterprise infrastructure projects of countries along the Belt and Road. Such a result has significant scientific reference value to future risk management and control in coal enterprises. Thus, some suggestions are now given that are related to risk types with a maximum probability of occurrence in the coal enterprises of Belt and Road Initiative countries along the Belt and Road. We expect that enterprises can improve their understanding of national laws and of culture, and strengthen their capacities to resist natural disasters such as rain and droughts. It is also necessary to enhance their employees' ability to manage emergencies. In particular, unwise investment may be avoided if investors and managers recognize opportunities and risks. They are recommended to organize resources carefully and prepare risk controls and strategies in advance to ensure energy infrastructure projects’ successful implementation in terms of efficiency and overall goals.
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Acknowledgments This work was supported by the National Natural Science Foundation of China (grant numbers 71273208 , 71271169 and 51874237).
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