Rapid urbanization has led to a surge in the number of towering structures, and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections. The complexity of the construction process makes the construction risk have certain randomness, so this paper proposes a cloud-based coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures. In the pretended model, the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element, and calculate the cloud correlation of the risk assessment metrics through the correlation algorithm of the cloud model to build the computational model. Meanwhile, the improved hierarchical analysis method based on the cloud model is used to determine the weight of the index. The comprehensive evaluation scores of the evaluation event are then obtained through the weighted average method, and the safety risk level is determined accordingly. Through empirical analysis, (1) the improved hierarchical analysis method based on the cloud model can incorporate the data of multiple decision-makers into the calculation formula to determine the weights, which makes the assessment results more credible; (2) the evaluation results of the cloud-based matter-element coupled model method are basically consistent with those of the other two commonly used methods, and the confidence factor is less than 0.05, indicating that the cloud-based physical element coupled model method is reasonable and practical for towering structure overturning; (3) the cloud-based coupled element model method, which confirms the reliability of risk level by performing Spearman correlation on comprehensive assessment scores, can provide more comprehensive information of instances compared with other methods, and more comprehensively reflects the fuzzy uncertainty relationship between assessment indexes, which makes the assessment results more realistic, scientific and reliable.
The concept of risk and risk assessment has a long history; as early as more than 2400 years ago, the Athenians suggested that they could assess risk before making decisions [
In terms of risk assessment methods, existing studies have made important contributions to the safety risk assessment of overturning construction of towering structures. Literature [
Through the summary and analysis of the relevant risk assessment methods, it can be found that the commonly used assessment methods can be divided into subjective and objective evaluation methods. Subjective evaluation methods include Delphi method, hierarchical analysis, etc.; the objective ones include gray system evaluation, matter-element extension method [
Therefore, this paper proposed a novel risk assessment method for overturning construction of towering structures. The main contributions of this paper are as follows. (1) An index system for overturning construction of towering structures is established on the basis of overturning construction process to deal with the problem of complex formwork construction technology and numerous construction projects. (2) The cloud model is used to improve the hierarchical analysis method to determine the index weights and avoid the adverse impact of over-reliance on expert opinions. (3) The risk assessment of overturning construction of towering structures is conducted by the cloud matter–element coupled model to estimate the uncertainty of different evaluation indexes scientifically. (4) A specific project in Xinjiang Province, China, is selected for the case study, and the research results provide new ideas for the safety risk assessment of similar projects.
This paper consists of five sections.
The purpose of this section is to establish a safety risk assessment model for turnover form-work construction of towering structures. This section contains five aspects: (1) Establishment of construction safety risk assessment index system by risk identification; (2) Grading of assessment indexes and safety risk metrics; (3) Determination of risk assessment index weights; (4) Introduction of the basic idea of cloud matter–element coupling model; (5) Creation of the construction safety risk assessment model based on the first four parts. The specific evaluation process is shown in
The first step of risk assessment is to identify risk factors [
The factors affecting the installation of formwork include wind load calculation [
For the construction of towering structures with turnover formwork on site, the risks involved in concrete placement operations cannot be ignored. During the pouring process, considering the influence of concrete slump, newly poured concrete lateral pressure [
Factors, such as the strength of the concrete before the formwork is lifted, the test hoisting, and the wind load factors at high altitude, are all important influencing features that constitute the construction risk during the turnover formwork upgrading stage [
The risk factors influencing the removal of turnover formwork [
The resulting hierarchy of risk indicators for overturning the construction of towering structures is shown in
The selected evaluation indexes must first be judged for the safety status to evaluate the safety risk status of the overturning construction of towering structures based on the published and implemented “Technical Regulations for Concrete in Tall Buildings” (JGJ3-2010) [
Security risk level | Possibility description | Severity of consequences |
---|---|---|
Low risk (V) | Hardly ever ( |
Negligible |
Lower risk (IV) | Hardly (0.01% ≤ |
To be considered |
Medium risk (III) | Occasionally (0.1% ≤ |
Serious |
Higher risk (II) | Possibly (1% ≤ |
Very serious |
High risk (I) | Frequently ( |
Catastrophic |
The commonly used weight determination methods are gray correlation, hierarchical analysis, and fuzzy evaluation. Among these methods, hierarchical analysis is widely used because of its simple operation and concise system. However, the hierarchical analysis method also has the following disadvantages: failure to solve the conflicts of multiple decision making during comprehensive evaluation and incomprehensive and subjective constructed judgment matrix. Simultaneously considering the fuzziness and randomness of the problem is also difficult. Therefore, this paper aims to improve the traditional hierarchical analysis method by using the cloud model to overcome the shortcomings of excessive dependence on subjective experience to determine the index weights.
Based on the concept of fuzzy set theory and probability statistics, the cloud model theory was developed by a Chinese scholar, Academician Li et al. [
The matter–element extension theory is a novel creation of Chinese scholar Cai in 1983 [
The cloud model was used in this study to transform the judgment matrix in AHP. The numerical judgment matrix was utilized to express the decision information quantitatively through the 1–9 scaling method. Assuming the existence of the theoretical domain
The final numerical characteristics of the critical scale cloud model are shown in
Important scale | Definition |
---|---|
The degree of importance is between the two adjacent clouds mentioned above | |
According to the above decision method, the cloud model scalar judgment matrix for the clouded hierarchical analysis was established in the form of
The entropy and hyperentropy of the elements on the diagonal in the above equation were 0 and the expectation was 1; n is the number of evaluation indicators. When the indicators were compared two by two, the
The obtained evaluation results of the experts were aggregated, and the average value was taken. The aggregation formula is shown in
The matrix was normalized and the multiplication calculation was introduced in cloud computing, and the relative weights
The expected consistency test was performed by consistency metrics C and R. This test required satisfying
The cloud matter–element was used in this paper to utilize the cloud model to redefine and construct the matter–element extension theory and to employ the general steps of extension evaluation to describe and evaluate things. The cloud matter–element [
In the cloud matter–clement model, when dividing the interval of safety risk level for the overturning construction of towering structures, the fuzzy and random nature of the cloud model was used to fuzzy the interval boundary values of the traditional matter–element model. That is to say, the hierarchical boundary of the classification level boundary of each evaluation index for the safety risk of turnover formwork construction of towering structures can help obtain the expectation
At present, the evaluation of indicators has two main methods of cloud entropy calculation:
The introduction of clouds has changed the ambiguity and randomness of the traditional matter–element theory in determining the grade boundaries. Thus, the calculation of the correlation degree of the matter–element model combined with the cloud model has also changed. If each assessed value of the turnover formwork construction to be evaluated
According to
The integrated evaluation vector
A weighted average method was applied to the composite assessment vector
When calculating the correlation degree, the presence of normal random numbers produced common calculation results. Therefore, the combined evaluation score of expectation
A large
The project is located in Turpan City, Xinjiang, China. The climatic conditions of the project location are remarkably extreme, with frequent windy weather in summer belonging to the continental monsoon climate and four indistinguishable seasons. Thus, the impact of the natural environment on construction cannot be ignored. The structure of the project is a reinforced concrete cylindrical tower structure with a building height of
The importance of risk indicators affecting safety was ranked by familiarizing with relevant project information and consulting with relevant experts. In this paper,
Numbers | Job position | Age | Education | Field | Engaged in engineering construction industry |
---|---|---|---|---|---|
1 | Project-level leadership | 43 | Bachelor | Project supervision | 11–20 years |
2 | Middle manager | 45 | Bachelor | Safety management | 11–20 years |
3 | Middle manager | 38 | Bachelor | Project supervision and consultation | 11–20 years |
4 | Top manager | 48 | Master | Project management | 21–30 years |
5 | Top manager | 52 | Bachelor | Construction design | 21–30 years |
6 | Project-level leadership | 43 | Master | Project consultation | 6–10 years |
7 | Middle manager | 47 | Master | Construction management | 21–30 years |
8 | Professor, PhD supervisor | 45 | PhD. | Risk management | 11–20 years |
9 | Professor, PhD supervisor | 42 | PhD. | Project management | 11–20 years |
10 | Professor, PhD supervisor | 53 | PhD. | Risk management | 21–30 years |
11 | Project-level leadership | 40 | Bachelor | Construction design | 11–20 years |
12 | Middle manager | 36 | Master | Project management | 6–10 years |
13 | Professor, PhD supervisor | 45 | PhD. | Structural Engineering | 11–15 years |
14 | Project-level leadership | 42 | Bachelor | Construction design | 11–20 years |
15 | Project-level leadership | 39 | Master | Construction management | 11–15 years |
16 | Professor, PhD supervisor | 54 | PhD. | Safety management | 21–30 years |
17 | Middle manager | 42 | Bachelor | Construction management | 11–20 years |
18 | Project-level leadership | 41 | Bachelor | Project supervision | 11–15 years |
19 | Project-level leadership | 44 | Bachelor | Project consultation | 11–20 years |
20 | Middle manager | 39 | Master | Construction management | 6–10 years |
1 | 5 | 3 | 4 | 1 | 7 | 3 | 5 | 1 | 7 | 4 | 3 | |
1/5 | 1 | 1/5 | 1/4 | 1/7 | 1 | 1/4 | 1/4 | 1/7 | 1 | 1/5 | 1/4 | |
1/3 | 5 | 1 | 1/5 | 1/3 | 4 | 1 | 1/3 | 1/4 | 5 | 1 | 1/3 | |
1/4 | 4 | 5 | 1 | 1/5 | 4 | 3 | 1 | 1/3 | 4 | 3 | 1 |
The cloud model-based linguistic judgment scales
1 | 2 | 3 | 4 | |
---|---|---|---|---|
1 | ||||
2 | ||||
3 | ||||
4 |
The relative weights calculated by
The consistency test was performed by
For the second-level indicators under the risk of template design and installation, Expert 1 believed that the relative importance of the four risk factors was ranked as
For the second-level indicators under the risk of concrete pouring, Expert 1 believed that the relative importance of the three risk factors was ranked as
For the second-level indicators under the risk of template upgrading, Expert 1 believed that the relative importance of the three risk factors was ranked as
For the second-level indicators under the risk of formwork removal, Expert 1 believed that the relative importance of the four risk factors was ranked as
1 | 3 | 7 | 5 | 1 | 4 | 6 | 4 | 1 | 2 | 6 | 5 | |
1/3 | 1 | 4 | 2 | 1/4 | 1 | 3 | 1/2 | 1/2 | 1 | 4 | 3 | |
1/7 | 1/4 | 1 | 1/2 | 1/6 | 1/3 | 1 | 1/4 | 1/6 | 1/4 | 1 | 1/2 | |
1/5 | 1/2 | 2 | 1 | 1/4 | 2 | 4 | 1 | 1/5 | 1/3 | 2 | 1 |
1 | 5 | 3 | 1 | 7 | 3 | 1 | 7 | 4 | |
1/5 | 1 | 1/5 | 1/7 | 1 | 1/4 | 1/7 | 1 | 1/5 | |
1/3 | 5 | 1 | 1/3 | 4 | 1 | 1/4 | 5 | 1 |
1 | 3 | 2 | 1 | 6 | 3 | 1 | 4 | 3 | |
1/3 | 1 | 1/3 | 1/6 | 1 | 1/4 | 1/4 | 1 | 1/5 | |
1/2 | 3 | 1 | 1/3 | 4 | 1 | 1/3 | 5 | 1 |
1 | 5 | 1 | 4 | 1 | 7 | 2 | 5 | 1 | 6 | 3 | 5 | |
1/5 | 1 | 1/3 | 1/4 | 1/7 | 1 | 1/4 | 1/3 | 1/6 | 1 | 1/5 | 1/2 | |
1 | 3 | 1 | 2 | 1/2 | 4 | 1 | 2 | 1/3 | 5 | 1 | 3 | |
1/4 | 4 | 1/2 | 1 | 1/5 | 3 | 1/2 | 1 | 1/5 | 2 | 1/3 | 1 |
The same method was used to calculate the weights under different indicators in turn. With the inclusion of the second-level index, the weight vectors of the template design and installation, concrete pouring, template upgrading, and formwork removal were
First level indicator | Weights | Secondary indicator | Weights | Combined weights |
---|---|---|---|---|
Wind load calculation (KN/m) | 0.553 | 0.303 | ||
Pressure calculation of the main beam (KN) | 0.225 | 0.123 | ||
Template design and installation | 0.547 | Pull-out test of computation of buried parts (KN) | 0.067 | 0.036 |
Permissible deviation of verticality of formwork installation (mm) | 0.155 | 0.085 | ||
Slump effect (mm) | 0.655 | 0.036 | ||
Newly poured concrete lateral pressure (KN) | 0.078 | 0.004 | ||
Concrete pouring | 0.055 | Concrete pouring speed under the influence of wind load (m/h) | 0.267 | 0.015 |
Strength of concrete before formwork lifted (MPa) | 0.592 | 0.086 | ||
Template upgrading | 0.145 | The test hoisting | 0.105 | 0.015 |
Wind load factor at high altitude (KN) | 0.303 | 0.044 | ||
Formwork removal | 0.253 | The location of the center of gravity of the template | 0.509 | 0.129 |
Percentage of concrete design strength achieved (%) | 0.066 | 0.017 | ||
The order of formwork removal | 0.288 | 0.073 | ||
Falling objects from height | 0.137 | 0.034 |
The index system contained 4 qualitative indicators and 10 quantitative indicators. For quantitative indicators, the data from public sources, such as statistical yearbooks and design specifications, are used for the interval division criteria; for qualitative indicators, levels I, II, III, IV, and V were respectively assigned 2, 4, 6, 8, and 10, as shown in
First level indicator | Secondary indicator | Risk level | ||||
---|---|---|---|---|---|---|
I | II | III | IV | V | ||
Wind load calculation (KN/m) | (4, 6.2) | (6.2, 7.4) | (7.4, 8.6) | (8.6, 9.8) | (9.8, 11.0) | |
Pressure calculation of the main beam (KN) | (30.76, 42.76) | (42.76, 44.76) | (44.76, 46.76) | (46.76, 48.76) | (48.76, 50.76) | |
Template design and installation | Pull-out test of computation of buried parts (KN) | (50.46, 61.46) | (61.46, 63.46) | (63.46, 65.46) | (65.46, 67.46) | (67.46, 69.46) |
Permissible deviation of verticality of formwork installation (mm) | (4, 5) | (3, 4) | (2, 3) | (1, 2) | (0, 1) | |
Slump effect (mm) | (180, 200) | (160, 180) | (130, 160) | (90, 130) | (50, 90) | |
Newly poured concrete lateral pressure (KN) | (70.36, 76.36) | (76.36, 78.36) | (78.36, 80.36) | (80.36, 82.36) | (82.36, 84.36) | |
Concrete pouring | Concrete pouring speed under the influence of wind load (m/h) | (8, 10) | (6, 8) | (4, 6) | (2, 4) | (0, 2) |
Template upgrading | Strength of concrete before formwork lifted (MPa) | (0, 15) | (15, 20) | (20, 25) | (25, 40) | (40, 50) |
The test hoisting | (0, 2) | (2, 4) | (4, 6) | (6, 8) | (8, 10) | |
Wind load factor at high altitude (KN) | (9, 10) | (7, 9) | (5, 7) | (3, 5) | (1, 3) | |
The location of the center of gravity of the template | (0, 2) | (2, 4) | (4, 6) | (6, 8) | (8, 10) | |
Formwork removal | Percentage of concrete design strength achieved (%) | (0, 50) | (50, 60) | (60, 75) | (75, 90) | (90, 100) |
The order of formwork removal | (0, 2) | (2, 4) | (4, 6) | (6, 8) | (8, 10) | |
Falling objects from height | (0, 2) | (2, 4) | (4, 6) | (6, 8) | (8, 10) |
After obtaining the grade division index, the standard cloud model of each secondary index was calculated by the method of determining the cloud model parameters. The specific numerical calculation results are shown in
Safety risk level | Boundary cloud model parameter values for each assessment index classification level |
||||
---|---|---|---|---|---|
U1-1 | U1-2 | U1-3 | U1-4 | U2-1 | |
High risk (I) | (5.1, 0.934, 0.08) | (36.76, 5.096, 0.08) | (55.96, 4.671, 0.08) | (10, 1.699, 0.08) | (190, 8.493, 0.08) |
Higher risk (II) | (6.8, 0.51, 0.08) | (43.76, 0.849, 0.08) | (62.46, 0.849, 0.08) | (7, 0.849, 0.08) | (170, 8.493, 0.08) |
Medium risk (III) | (8, 0.51, 0.08) | (45.76, 0.849, 0.08) | (64.46, 0.849, 0.08) | (4.5, 1.274, 0.08) | (145, 12.74, 0.08) |
Lower risk (IV) | (9.2, 0.51, 0.08) | (47.76, 0.849, 0.08) | (66.46, 0.849, 0.08) | (2, 0.849, 0.08) | (110, 16.987, 0.08) |
Low risk (V) | (10.4, 0.2, 0.08) | (49.76, 0.333, 0.08) | (68.46, 0.333, 0.08) | (0.5, 0.167, 0.08) | (70, 6.667, 0.08) |
U2-2 | U2-3 | U3-1 | U3-2 | U3-3 | |
High risk (I) | (73.36, 2.548, 0.08) | (9, 0.849, 0.08) | (7.5, 6.37, 0.08) | (1, 0.849, 0.08) | (9.5, 0.425, 0.08) |
Higher risk (II) | (77.36, 0.849, 0.08) | (7, 0.849, 0.08) | 17.5, 2.123, 0.08) | (3, 0.849, 0.08) | (8, 0.849, 0.08) |
Medium risk (III) | (79.36, 0.849, 0.08) | (5, 0.849, 0.08) | (22.5, 2.123, 0.08) | (5, 0.849, 0.08) | (6, 0.849, 0.08) |
Lower risk (IV) | (81.36, 0.849, 0.08) | (3, 0.849, 0.08) | (32.5, 6.37, 0.08) | (7, 0.849, 0.08) | (4, 0.849, 0.08) |
Low risk (V) | (83.36, 0.333, 0.08) | (1, 0.333, 0.08) | (45, 1.667, 0.08) | (9, 0.333, 0.08) | (2, 0.333, 0.08) |
U4-1 | U4-2 | U4-3 | U4-4 | ||
High risk (I) | (1, 0.849, 0.08) | (25, 21.233, 0.08) | (1, 0.849, 0.08) | (1, 0.849, 0.08) | |
Higher risk (II) | (3, 0.849, 0.08) | (55, 4.247, 0.08) | (3, 0.849, 0.08) | (3, 0.849, 0.08) | |
Medium risk (III) | (5, 0.849, 0.08) | (67.5, 6.37, 0.08) | (5, 0.849, 0.08) | (5, 0.849, 0.08) | |
Lower risk (IV) | (7, 0.849, 0.08) | (82.5, 6.37, 0.08) | (82.5, 6.37, 0.08) | (7, 0.849, 0.08) | |
Low risk (V) | (9, 0.333, 0.08) | (95, 1.667, 0.08) | (95, 1.667, 0.08) | (9, 0.333, 0.08) |
The MATLAB program was prepared in accordance with the three numerical characteristics of the cloud model of the classification level boundaries of each assessment index in
By investigating the field situation and schedule of the construction site, three groups of working conditions with different orientations of the same construction height were selected in this study. The actual measured or score values of the 13 evaluation indicators in were inputted in
Item | U1–1 | U1–2 | U1–3 | U1–4 | U2–1 | U2–2 | U2–3 | U3–1 | U3–2 | U3–3 | U4–1 | U4–2 | U4–3 | U4–4 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 9.8 | 36.67 | 67.46 | 8 | 120 | 82.36 | 2.5 | 30 | 3 | 4.5 | 3 | 90 | 5 | 4 |
2 | 9.6 | 34.20 | 65.40 | 8 | 125 | 80.30 | 3 | 25 | 3 | 4.5 | 3 | 80 | 4 | 4 |
3 | 9.8 | 35.00 | 65.80 | 7 | 120 | 82.00 | 3 | 30 | 2 | 5 | 2 | 90 | 5 | 4 |
The sample evaluation vector
Meanwhile, the evaluation results were compared with the conventional matter–element method and cloud model to verify the effectiveness of the method in this paper. The results of the final evaluation are shown in
Work condition | Method of this article | Evaluation results of the matter-element method | Evaluation results of cloud model method | |
---|---|---|---|---|
Evaluation results | Confidence factors | |||
1 | III |
0.0041 | III | III |
2 | III |
0.0036 | III | IV |
3 | III |
0.0052 | III | III |
Sensitivity Analysis is performed based on the risks index system of overturning construction of towering structure. When the weights of risk index in
As we can see from
From above sensitivity analysis, we can make a conclusion that
The evaluation results of the cloud-based matter–element coupled model in this paper had high similarity individually compared with the two other commonly used methods. Among them, the assessment results of the proposed method in this paper were consistent with 100% compared with the traditional matter–element method, and two-thirds of the assessment results were consistent compared with the cloud model method. In the two other methods, the proportion of level III was 83.3%. Thus, the assessment results of the cloud matter–element coupled model were representative.
In addition, the cloud matter-coupled model defined the safety risk level by using a comprehensive assessment score mean
This paper first adopted the cloud matter–element coupled model to solve the complex uncertainty problem of safety risk assessment of overturning construction of towering structures effectively. This model maximized the advantages of matter–element theory and cloud model to deal with the fuzziness and randomness in the safety risk assessment of the overturning the construction of towering structures. The improved hierarchical analysis method was then employed on the basis of the cloud model through the aggregation algorithm of the cloud model to bring all the assignments of multi-person decision making into the calculation formula. The determination of the weights of various construction risk indicators was more objective and reliable. Finally, a reinforced concrete cylindrical tower structure overturning construction safety risk assessment was taken as a case to describe the application steps of the proposed method. The evaluation results of the proposed method are also compared with those of the traditional matter–element and cloud model methods to test the application effect of the proposed method. The findings showed that the assessment results of the cloud matter–element coupled model method were consistent with those of two other common methods. Moreover, the confidence factor of each assessment case was less than 0.05, which indicated that the proposed method in this paper was effective in the safety risk assessment of overturning the construction of towering structures. The proposed model in this paper used a comprehensive assessment score mean to determine the safety risk level, which can provide more integrated information regarding the assessment cases compared with other assessment methods, and reflect the uncertain relationship among evaluation indexes more comprehensively compared with other evaluation methods, thus increasing the reliability of the evaluation results.
However, in the process of safety risk assessment of towering structure overturning construction, the classification criteria of the safety risk level of each assessment index are not constant, and the scoring value of the assessment index is susceptible to subjective factors, both of which have an impact on the reasonableness of the assessment results. Therefore, further supplementing and improving the safety risk assessment index system of towering structure overturning construction is necessary for the subsequent research. Moreover, the issues, such as the scoring value of the assessment index and its safety risk classification criteria, should be investigated further to improve the scientificity of the assessment index system and enhance the comprehensiveness and objectivity of the assessment results.
This research was funded by
The authors declare that they have no conflicts of interest to report regarding the present study.