To extract readable knowledge, Fuzzy IF-THEN linguistic rules are adopted for the knowledge representation of the proposed system. A standard FALCON consists of five layers: (1) Layer 1 - input attribute values are transmitted directly to FALCON; (2) In Layer 2 - fuzzification of input attribute values from Layer 1 is performed, so that the input data is converted into fuzzy sets (or linguistic terms); (3) Layer 3—the connections between the second and third layers represent the preconditions of fuzzy IF-THEN rules; (4) Connections between Layer 3 and Layer 4 - represent the consequences of fuzzy rules; (5) Layer 5 - acts as a disambiguator to infer fuzzy reasoning from FALCON. The computational algorithms of the proposed HSCS consist of three categories: (1) Self-organization; (2) Supervised learning; (3) Global search.
In the update phase, the center of the ith cluster, the winning cluster, is adjusted to the incoming training data, x, at the kth iteration (w$ik). In the winner competition phase, the strongest response node of the output expression layer is found. In supervised learning, the parameters of the membership functions for the input and output linguistic expressions are fine-tuned by back-propagation (BP) algorithms.
The integrated learning process of the proposed system is shown in Figure 3, where the learning process is divided into two phases: NFS with roughly determined centers and distributions of the fuzzy membership functions in both input and output layers of the VaFALCON. In Phase II, the parameters of the membership function in the output and input terms and the resulting connections of fuzzy IF-THEN rules.
The capability of knowledge discovery should represent the ability of the DM algorithm to find out interesting patterns and rules behind the data. In this research, a simple index of DM capability, accuracy, is adopted for measuring the DM capability for knowledge discovery of the proposed HSCS. The scarcity of data can be measured by the ratio of the number of variables in the model over the number of available training sets.
It is noted that the first and third terms on the right-hand side are multiplied by a coefficient, 2, which counts for the number of the undetermined variables, centers and spreads of the membership functions. The experiment is then designed to test the data from the three cases with different degrees of Rs to see its impact on the accuracy of the system defined in Equation (9). The experiment is then designed to test the data from the three cases with different degrees of POI to see its impact on the accuracy of the system.
The experiment aims to test the data of the three cases with different degrees of p% to see its impact on the system accuracy. To compare the test results, the fuzzy partitions of the three cases were checked in the same way as those in the literature. It is concluded that HSCS is more suitable for mining sparse construction databases compared to most other soft computing methods.
The fuzzy membership functions of the linguistic terms for the input/output attributes of the three cases are shown in Figures 8 to 10 , respectively.
DISCUSSION
IF type-earth-retain-method Simple is AND Number-of-floors-above-ground is Small AND Number-of-floors-underground is Small AND total-floor-area is Small, . By examining all fuzzy IF-THEN rules, the knowledge obtained from the data mining process can be manually verified. However, with proper design of pre- and post-processing functions, the above DM functionalities can also be implemented.
The validation methodology applied in this study is based on a simple index, accuracy, of the classification function of the proposed HSCS. It should be noted that classification performs the fundamental functions for all other DM functionalities.
CONCLUSION AND RECOMMENDATION
Although HSCS is able to extract fuzzy IF-THEN rules that can be visualized and verified by domain experts; the resulting fuzzy rule base is too large for human experts to manually verify. Some rule pruning or screening method must be developed to reduce the rule base to make such a system realistic for practical use. Moreover, other soft computing techniques, such as C4.5 and C5.0, can be considered in comparison with the proposed HSCS in future works.
Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Diego, USA Hendrickson, C., Construction Project Management - Basic Concepts for Owners, Engineers, Architects and Builders, Prentice Hall, New York, USA, 1998. . - S.; Chen, C.-N.; and Chang, S.-L., "Data mining for tunnel support stability: neural network approach", Automation in Construction, Vol. Fuzzy Sets and Systems, Vol. 2002) “Data preparation process for knowledge generation in construction through knowledge discovery in databases”, Journal of Computing in Civil Engineering, Vol. 2003) "Design Review Control System with Corporate Lessons Learned", Journal of Construction Engineering and Management, Vol.
IEEE Transactions on Neural Networks, Vol. 1997) “An Integrated Model of Knowledge Acquisition and Problem Solving for Experience-Oriented Problems in Construction Management,” Dissertation in Partial Fulfillment of Ph.D. Requirements, National Central University, Chungli,. Incorporating case-based reasoning and expert system techniques to solve experience-oriented problems”, Journal of the Chinese Institute of Engineers, Vol. 1998) “Using Case Based Thinking Techniques in Retaining Wall Selection,” Automation in Construction, Vol. 2006) “VaFALCON Neuro-Fuzzy System for Mining Incomplete Construction Databases,” Automation in Construction, Vol. 2006) “Hybridization of CBR and Numerical Soft Computing Techniques for Mining Sparse Construction Databases.”.
一种用于施工技术评估的可施工知识获取的神经模糊计算方法。”《建筑自动化杂志》，卷。项目名称：建筑知识发现中的混合柔性计算系统研究-第三年项目协调员：余文德。用于复杂构建数据库知识挖掘的混合柔性计算机系统（HSCS） 所提出的 HSCS 结合了模糊逻辑、神经网络、.
混合遗传算法等灵活的计算技术提供了具有强大探索能力的工具。经过三种灵活的计算机测试（数据不足、数据缺失和不确定性），发现HSCS可以有效地发现复杂数据的隐性知识。该 HSCS 对于建筑公司的智能开发和其他相关应用具有潜在价值。所提出的方法混合了模糊逻辑、人工神经网络（ANN）和杂乱遗传算法（mGA）等软计算技术，形成一个新系统。从历史数据库中挖掘人类可理解的知识的计算方法。混合软计算系统（HSCS）是为挖掘正在建设的复杂数据库而开发的，该数据库具有稀缺性、不完整性和不确定性三个特征。
选择真实的施工数据存储库来测试所提出的HSCS在上述复杂条件下的数据挖掘能力，测试结果表明所提出的HSCS在挖掘复杂施工数据库方面的巨大潜力。
它对具有数据不足、数据缺失和不确定性三个交互特征的数据库具有强大的数据探索能力。
