# AFuzzyLogic-BasedMethodforRiskAssessmentofBridgesduringConstruction.pdf

Journal of Harbin Institute of Technology(New series) 哈尔滨工业大学学报(英文版) ISSN 1005-9113,CN 23-1378/T 《《Journal of Harbin Institute of Technology(New series)》 网络首发论》 网络首发论 文文 题目： A Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction 作者： Jin Cheng，Mingsai Xu，Zhengrong Chen 收稿日期： 2018-07-29 网络首发日期： 2018-10-18 引用格式： Jin Cheng， Mingsai Xu， Zhengrong Chen． A Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction[J/OL]．Journal of Harbin Institute of Technology(New series). http://kns.cnki.net/kcms/detail/23.1378.T.20181016.0944.002.html 网络首发网络首发：在编辑部工作流程中，稿件从录用到出版要经历录用定稿、排版定稿、整期汇编定稿等阶 段。录用定稿指内容已经确定，且通过同行评议、主编终审同意刊用的稿件。排版定稿指录用定稿按照期 刊特定版式（包括网络呈现版式）排版后的稿件，可暂不确定出版年、卷、期和页码。整期汇编定稿指出 版年、 卷、 期、页码均已确定的印刷或数字出版的整期汇编稿件。录用定稿网络首发稿件内容必须符合《出 版管理条例》和《期刊出版管理规定》的有关规定；学术研究成果具有创新性、科学性和先进性，符合编 辑部对刊文的录用要求，不存在学术不端行为及其他侵权行为；稿件内容应基本符合国家有关书刊编辑、 出版的技术标准，正确使用和统一规范语言文字、符号、数字、外文字母、法定计量单位及地图标注等。 为确保录用定稿网络首发的严肃性，录用定稿一经发布，不得修改论文题目、作者、机构名称和学术内容， 只可基于编辑规范进行少量文字的修改。 出版确认出版确认：纸质期刊编辑部通过与《中国学术期刊（光盘版） 》电子杂志社有限公司签约，在《中国 学术期刊（网络版） 》出版传播平台上创办与纸质期刊内容一致的网络版，以单篇或整期出版形式，在印刷 出版之前刊发论文的录用定稿、排版定稿、整期汇编定稿。因为《中国学术期刊（网络版） 》是国家新闻出 版广电总局批准的网络连续型出版物（ISSN 2096-4188，CN 11-6037/Z） ，所以签约期刊的网络版上网络首 发论文视为正式出版。 Journal of Harbin Institute of Technology (New Series) Received 2018-07-29. Sponsored by the Ministry of Science and Technology of China (Grant No.SLDRCE14-B-08). *Corresponding author. Winner of the Program for New Century Excellent Talents in University of Ministry of Education. E-mail: chengjin@tongji.edu.cn. DOI:10.11916/j.issn.1005-9113.18089 Title: A Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction Authors: Jin Cheng 1,2*, Mingsai Xu2 and Zhengrong Chen2 (1. State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China; 2. Department of Bridge Engineering, Tongji University, Shanghai 200092, China) Accepted manuscript and uncorrected proof: This article has been peer reviewed and accepted for publication by the Editorial Board. It has not yet been formatted in the publication house style, and still need to be proof-read and corrected by the author(s) and the text could still change before final publication. Journal of Harbin Institute of Technology (New Series) ISSN:1005-9113 E-mail：hitxuebao_e@hit.edu.cn http://hit.alljournals.cn/jhit_cn/ch/index.aspx Published online：2018-10-18 19:37:55 URL：http://kns.cnki.net/kcms/detail/23.1378.T.20181016.0944.002.html Journal of Harbin Institute of Technology (New Series) DOI:10.11916/j.issn.1005-9113.18089 A Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction Jin Cheng 1,2*, Mingsai Xu2 and Zhengrong Chen2 (1. State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China; 2. Department of Bridge Engineering, Tongji University, Shanghai 200092, China) Abstract: There are many potential sources of risks which may cause bridge failures and result in numerous economic and human losses during the construction of bridges. Therefore, risk assessment for bridges during construction should be taken rigorously to avert bridge failures and casualties. This article presents a fuzzy logic-based method which integrates the fuzzy analytical hierarchy process (FAHP) method based on a 3-point scale, fuzzy logic, and fuzzy set theory into a single synthetic method. In this method, the FAHP method based on a 3-point scale was used to identify and rank diverse risk factors, and fuzzy logic and fuzzy set theory were used to process inaccurate datasets including non-statistical information. After the concept and procedure of the FAHP method based on a 3-point scale were demonstrated, the proposed fuzzy logic-based method was used to perform risk assessment on the Aizhai Suspension Bridge with a main span length of 1176 m in China. The results show that the proposed method can more effectively carry out risk assessment of bridges during construction. Keywords: analytical hierarchy process, risk assessment, fuzzy logic, fuzzy consistent matrix, bridge construction CLC number: U44 Document code: A 1 Instruction Bridges represent essential parts of transportation network. Due to the existence of various risk factors, unavoidable damage may occur to bridge structures, which leads to huge economic loss and casualties. Thus, it is necessary to rigorously perform risk assessment to avert accidents. Risk assessment is the process of evaluating risks related to a specific case. The aim of risk assessment for bridges during construction is to help decision-makers propose 1) reasonable security arrangements and 2) appropriate organization plans [1]. Compared with other phases, the construction stage of bridges is more likely to be plagued by various risk factors related to construction technologies, natural hazards, and human factors, which often give rise to extra costs, schedule delays, and even total project failure [2]. Nevertheless, literature suggests that risk analysis of bridges during construction has not been taken into account as equally as that of bridges in design, operation, or maintenance stage [3]. Risk identification, the first step of risk assessment of bridges during construction, is an extremely complex issue and has important impact on the validity of the subsequent risk analysis and control. As a matter of fact, finding out more probable risk factors among the many risk factors can be regarded as a multi-criteria decision making (MCDM) problem, in which the criteria should meet various requirements. The analytical hierarchy process (AHP) method has become one of the most commonly used approaches to solve various decision-making problems over the past several decades. The principle behind this method is that by organizing the key factors of an MCDM problem into a hierarchical structure, the analysts are able to make the best decision on the basis of a set of simple comparisons and rankings instead of complex decisions. Therefore, the AHP method can be used for bridge risk identification. Since Saaty[4] proposed the AHP method in the mid-1970s, several efforts have been made to Journal of Harbin Institute of Technology (New Series) improve this method and enlarge its applicable range. So far, the AHP method has made rapid progress in a variety of fields such as optimization, best alternative selecting, planning, etc[5-14]. However, the AHP method is unable to handle the intrinsic inaccuracy and vagueness derived from the transformation of one’s standpoint to a precise number. To tackle this problem, the FAHP method has been developed recently [15-16]. However, most approaches of the existing AHP and FAHP methods to solve MCDM problems are based on a 9-point scale of relative importance between Ci (i-th criterion) and Cj (j-th criterion), which can be referred to in Table 1. Although these methods have been widely used, the conventional AHP and FAHP methods have the following disadvantages: 1) fuzzy comparison matrices are hardly consistent in the course of risk identification by using the 9-point scale. Thus, pairwise comparisons process is required to be repeated, which may consume a large amount of time; 2) in practice, decision-makers usually find it extremely difficult to conduct exact pairwise comparison judgments by using a 9-point scale. To circumvent the above disadvantages, a risk assessment method combining the FAHP method based on a 3-point scale (see Table 2), fuzzy set theory, and fuzzy logic was proposed in this paper. By using “logical checking”, which only contains three cases: 1) “Ci” is equally important as “Cj”; 2) “Ci” is more important than “Cj”, and 3) “Ci” is less important than “Cj”, the process of constructing comparison matrix is greatly simplified. In the proposed method, fuzzy set theory and fuzzy logic can process inaccurate datasets consisting of non-numerical information. Aside from this feature, compared with other methods, the most valuable aspect of the fuzzy set theory is that it can be manipulated with linguistic variables since not every event can be represented numerically[17-19]. Literature indicates that in terms of risk assessment of bridges during construction, the application of this approach has not been studied before. Table 1 A 9-point scale of relative importance Relative importance Grading scale Ci and Cj are equally important 1 2 Ci is moderately more important than Cj 3 4 Ci is strongly more important than Cj 5 6 Ci is very strongly more important than Cj 7 8 Ci is extremely strongly more important than Cj 9 Table 2 A 3-point scale of relative importance Relative importance Grading scale Ci is less important than Cj 0 Ci and Cj are equally important 0.5 Ci is more important than Cj 1 The organizational structure of this paper is as follows. Section 2 presents the framework of fuzzy logic-based theory for risk assessment. Section 3 demonstrates the proposed fuzzy logic-based method, where the FAHP method based on a 3-point scale is described and primary steps of the proposed fuzzy logic-based method are briefly presented. Section 4 investigates a numerical example which undergoes bridge risk assessment to prove the validity of the proposed method. Finally, main conclusions are drawn in Section 5. 2 Framework of Fuzzy Logic-Based Theory for Risk Assessment As depicted in Fig. 1, the hierarchically structured framework of the proposed fuzzy logic-based method to assess the risk of bridges during construction is based on the current codes in China [20]. The framework includes risk identification, risk ranking, risk analysis, and risk assessment. The first step of fuzzy logic-based method is risk identification, which is to identify all Journal of Harbin Institute of Technology (New Series) potential risks and their specifics that could undermine structural safety in construction stage. According to their order of importance, risk factors identified previously should be prioritized. Risk ranking plays a vital role in risk assessment and is implemented on the basis of experts’ subjective standpoints. The next step is risk analysis, which is to evaluate impacts of identified risks and is the pivotal step in the overall process of risk assessment. It consists of two parts: 1) probability of occurrence (probability analysis); 2) risk losses (loss analysis). The probability of risk occurrence during bridge construction can be obtained by probability analysis, and loss analysis is performed according to effects of risks on communities, environment, and people. In general, parameters of risk factors, including probability of occurrence and risk losses, can be obtained by quantitative and qualitative methods. Quantitative methods employ mathematical models to identify potential risks and their specifics that could influence the project. Qualitative methods such as Delphi technique estimate risks on the basis of experts’ subjective judgments depending on their experience and expertise[21]. Risk analysis was conducted by referring to experts’ subjective judgments and was calculated through the processes of fuzzification and aggregation in this study, as depicted in Fig.1. Risk Identification Probability of OccurenceRisk Loss FuzzificationFuzzification AggregationAggregation Risk Ranking Aggregation Defuzzification Risk Analysis Fig. 1 Framework of fuzzy logic-based theory for risk assessment Risk assessment including the process of aggregation and defuzzification is the last step of the framework. Mathematically, the final risk value of a bridge during construction can be derived by the following equation: ? ? ? ∙ ? (1) where, P—probability of risk occurrence, L—risk losses. 3 Proposed Fuzzy Logic-Based Method for Risk Assessment of Bridges during Construction In this study, the proposed fuzzy logic-based method integrated the FAHP method based on a 3-point scale，fuzzy logic, and fuzzy set theory and used the same basic concept of the aforementioned framework for risk assessment. In this approach, the FAHP method based on a 3-point scale was used to construct and rank diverse risk factors of a bridge during construction. Then the final risk level of this bridge was derived by using the proposed fuzzy logic-based theory. 3.1 FAHP Method Based on 3-Point Scale for Risk Identification and Risk Ranking In order to perform risk identification and figure out the priority weights of risk factors, an FAHP method based on a 3-point scale consisting of fuzzy consistent matrix (FCM) method and AHP was developed in Ref.[22]. The structured pairwise comparison matrix can be converted to an FCM ?′ by FCM method. Since FCM is consistent, repeated consistency checking is not necessary. FAHP method based on a 3-point scale is not presented in this paper. Refer to Ref. [22] and [23] for more detailed introduction. 3.2 Fuzzy Logic-Based Theory for Risk Analysis and Risk Assessment In the proposed fuzzy logic-based method, risk analysis and risk assessment were conducted by utilizing fuzzy logic and fuzzy set theory, which can process inaccurate datasets consisting of non-numerical information. In this section, the procedure of risk analysis and risk assessment is outlined below, and more details can be found in Andric and Lu [1]. Journal of Harbin Institute of Technology (New Series) Step 1 Select the linguistic scale: Generally, we use linguistic variables to describe a specific issue. For example, to rank the probability of risk occurrence of a bridge, such a set of linguistic variables as very frequent (VF), frequent (F), moderate (M), rare (R), and very rare (VR) can be used. Similarly, to characterize the extent of risk losses, linguistic variables like very big (VB), big (B), moderate (M), small (S), and very small (VS) can be used. A set of linguistic variables like these is termed as linguistic scale. Mathematically, linguistic scale represents a group of fuzzy numbers. Trapezoidal and triangular fuzzy numbers are the most commonly used fuzzy numbers, and their membership functions are respectively defined as: ~ 2 () /(), 1, ( ) () /(), 0, otherwise A xabaaxb bxc x dxdccxd (2) ~ 1 ()/(), ( )()/(), 0, otherwise A xabaaxb xdxdbbxd (3) For simplicity, trapezoidal and triangular fuzzy numbers are generally expressed as (a, b, c, d) and (a, b, d)[24]. The linguistic scale is selected based on the characteristic of the fuzzy numbers which is suited to represent specific linguistic variables for a specific issue. Step 2 Collect linguistic data: In this step, subjective judgments of different experts are made in linguistic variables that have been selected in the previous step. The probability of risk occurrence and risk losses are assigned by experts in bridge engineering through questionnaire survey about every risk sub-factor. Step 3 Fuzzify the collected data: The collected linguistic data of every risk sub-factor is transformed to matching fuzzy n