Zhuojin Pipe Fitting Co., LtdNonlinear Fuzzy Evaluation of Construction Risks in Integral Hoisting of Steel Tube Concrete Tied-Arch Bridges
Literature Overview
The paper by An Lang and Zhang Xinran, published in Highway and Transport Research in 2015, proposes a nonlinear fuzzy evaluation method based on structural entropy weighting for assessing construction risks in the integral hoisting of steel tube concrete (CFST) tied-arch bridges. The authors, affiliated with the Xi'an Highway Research Institute and the Xi'an Municipal Facilities Administration, address the limitations of traditional fuzzy evaluation methods by incorporating nonlinear characteristics into the evaluation process. The research is supported by the Central Universities Basic Scientific Research Fund Specialized Project (CHD2009JC166). This study is particularly relevant to bridge engineers and construction managers involved in the design and execution of large-scale integral hoisting operations for composite arch bridges.
Core Technical Analysis
The integral hoisting of CFST tied-arch bridges involves lifting the entire arch structure, including the steel tubes and the concrete fill, as a single unit. This method offers significant advantages in terms of construction speed and structural continuity, but it also introduces unique risk challenges that differ from those of segmented construction. The risk sources in this process include structural risks related to the stability of the hoisted structure, equipment risks related to the lifting gear and winches, environmental risks related to wind and weather conditions, and personnel risks related to the safety of workers involved in the operation.
| Risk Category | Key Risk Sources | Severity Level | Mitigation Measures |
|---|---|---|---|
| Structural | Arch instability, concrete-steel interface failure, foundation settlement | High | Structural monitoring, load testing, progressive loading |
| Equipment | Winch failure, cable breakage, spreader bar deformation | High | Pre-use inspection, load testing, redundant equipment |
| Environmental | High wind, heavy rain, temperature extremes | Medium | Weather monitoring, wind speed limits, thermal expansion control |
| Personnel | Fall from height, crane strike, entanglement | Medium | Safety training, PPE, exclusion zones, emergency procedures |
| Management | Communication failure, scheduling errors, documentation gaps | Medium | Clear protocols, real-time communication, comprehensive documentation |
The structural entropy weighting method combines subjective weighting (based on expert judgment and the Analytic Hierarchy Process) with objective weighting (based on the entropy of the evaluation data). The subjective weights reflect the relative importance of each risk factor as determined by experienced engineers, while the objective weights reflect the information content of each risk indicator as measured by the variability of the evaluation data. The combined weight for each risk factor is calculated as w_i = α × w_i^s + (1-α) × w_i^o, where α is the subjective-objective weight ratio, typically set between 0.4 and 0.6.
The nonlinear fuzzy algorithm introduces a key innovation over traditional fuzzy evaluation. In traditional fuzzy evaluation, the membership function is typically linear or triangular, which treats all evaluation grades equally. The nonlinear fuzzy algorithm uses a nonlinear membership function that amplifies the influence of high-risk indicators on the overall evaluation result. This is achieved by applying a nonlinear transformation to the membership values, such as μ'_i = μ_i^k, where k is a nonlinear exponent greater than 1. This transformation ensures that a single high-risk indicator has a disproportionate effect on the overall risk level, which is more consistent with engineering practice where a single critical failure can lead to catastrophic consequences.
Risk Identification and Hierarchical Evaluation
The risk identification process follows the construction sequence of the integral hoisting operation. The first phase involves the preparation of the hoisting site, including the installation of temporary supports, the calibration of lifting equipment, and the establishment of communication protocols. The second phase involves the connection of the arch segments to the lifting gear, which requires precise alignment and secure attachment. The third phase involves the actual hoisting operation, during which the arch is lifted to its design elevation and the temporary supports are released. The fourth phase involves the final adjustment and connection of the arch to the bridge deck, which requires precise positioning and load transfer.
The hierarchical evaluation system organizes the risk sources into two levels: the first level consists of the major risk categories (structural, equipment, environmental, personnel, and management), and the second level consists of the specific risk indicators within each category. For example, the structural risk category includes indicators such as arch stability, concrete-steel interface integrity, foundation settlement, and connection joint strength. Each indicator is evaluated on a scale of 1 to 5, where 1 represents negligible risk and 5 represents catastrophic risk.
The nonlinear fuzzy evaluation model is constructed as follows: the membership vector for each risk indicator is determined by the nonlinear membership function, the weight vector is determined by the structural entropy weighting method, and the overall risk evaluation is calculated as the weighted sum of the membership values. The result is a composite risk score that reflects the nonlinear influence of high-risk indicators and the combined effect of all risk sources.
Engineering Practice and Case Study
The paper presents a case study of an integral hoisting operation for a CFST tied-arch bridge with a span of 120 meters. The arch was constructed in three segments, each weighing approximately 45 tons, and was hoisted using a pair of tower cranes with a combined lifting capacity of 100 tons. The risk evaluation was performed at each phase of the construction process, and the results were used to adjust the construction plan and implement additional safety measures.
The risk evaluation revealed that the structural risk during the hoisting phase was the highest, with a composite risk score of 4.2 out of 5. This was primarily due to the uncertainty in the load distribution among the lifting points and the potential for asymmetric loading during the hoisting operation. The evaluation recommended the implementation of real-time load monitoring at each lifting point, the use of a load-balancing system to equalize the loads, and the establishment of a strict wind speed limit of 15 m/s for the hoisting operation.
The equipment risk was evaluated as moderate, with a composite risk score of 3.1 out of 5. The primary concerns were the reliability of the winch systems and the condition of the lifting cables. The evaluation recommended the use of new cables with a safety factor of at least 6, the implementation of a pre-use inspection protocol for all lifting equipment, and the maintenance of backup winch systems on site.
The environmental risk was evaluated as moderate, with a composite risk score of 3.5 out of 5. The primary concerns were the wind conditions and the temperature variations during the hoisting operation. The evaluation recommended the establishment of a weather monitoring station at the construction site, the implementation of a wind speed limit, and the use of thermal expansion joints to accommodate temperature-induced dimensional changes.
Methodological Strengths and Limitations
The nonlinear fuzzy evaluation method proposed in this paper addresses a fundamental limitation of traditional fuzzy evaluation: the linear treatment of risk indicators. In engineering practice, the risk associated with a single critical failure is not merely the sum of the individual risks but is amplified by the potential for cascading failures. The nonlinear membership function captures this amplification effect, ensuring that the evaluation result is sensitive to the presence of high-risk indicators.
However, the method has limitations that must be acknowledged. The accuracy of the evaluation depends on the quality of the risk identification and the reliability of the expert judgments. If the risk sources are incompletely identified or if the expert judgments are biased, the evaluation result may be misleading. The method also assumes that the risk indicators are independent, which is not always the case in practice. For example, a high wind speed may increase both the structural risk and the personnel risk, creating a correlation that the independent evaluation model does not capture.
The structural entropy weighting method provides an objective basis for determining the weights, but the subjective-objective weight ratio α must be selected by the analyst. Different values of α produce different evaluation results, and the choice of α should be justified based on the specific context of the project. A sensitivity analysis should be performed to determine the range of α values that produce consistent evaluation results, and the evaluation should be reported for the entire range rather than for a single value.
Key Questions and Reflections
A critical question is how the nonlinear fuzzy evaluation method compares with other risk assessment methodologies such as Failure Mode and Effects Analysis (FMEA), Hazard and Operability Study (HAZOP), and quantitative risk analysis (QRA). Each methodology has its strengths and weaknesses, and the choice of methodology should be based on the complexity of the project, the availability of data, and the decision-making requirements. For integral hoisting operations, a combination of methodologies may be appropriate: FMEA for identifying potential failure modes, HAZOP for analyzing the construction process, and the nonlinear fuzzy evaluation for quantifying the overall risk level.
Another important consideration is the integration of the risk evaluation into the construction management process. The evaluation should not be a one-time activity but should be performed at each phase of the construction process, with the results used to update the risk register and adjust the construction plan. The evaluation should also be integrated with the project's safety management system, including the emergency response plan, the communication protocols, and the training programs.
Study Insights and Implications
This paper presents a methodologically sound approach to risk evaluation for integral hoisting of CFST tied-arch bridges. The nonlinear fuzzy algorithm provides a more realistic representation of the risk landscape than traditional linear methods, and the structural entropy weighting method offers a principled approach to combining subjective and objective information. The case study demonstrates the practical applicability of the method and its ability to identify critical risk sources that require additional mitigation measures.
For bridge engineers and construction managers, the key takeaway is that risk evaluation should be an integral part of the construction planning process, not an afterthought. The nonlinear fuzzy evaluation method provides a quantitative framework for prioritizing risk mitigation efforts and allocating resources to the most critical risk sources. The method should be adapted to the specific characteristics of each project, and the evaluation results should be communicated clearly to all stakeholders involved in the construction process.
In conclusion, the nonlinear fuzzy evaluation method based on structural entropy weighting is a valuable tool for assessing construction risks in integral hoisting of CFST tied-arch bridges. The method captures the nonlinear characteristics of risk and provides a quantitative basis for decision-making. Engineers should adopt this methodology as part of their risk management toolkit, while being aware of its limitations and complementing it with other risk assessment techniques as appropriate.