南冲高架桥某基桩的检测与处理

详细论述了南冲高架桥 6-2桩的检测过程 ,说明了岩溶地质对桩基质量的影响 ,并对此影响和预防措施进行了论述     

基础下早期人防工程处理的实践与探索

地下早期人防工程的处理是当前城市建设中经常遇到的问题。鲁南商城工程地上六层 ,地下一层 ,场区地下有一组早期人防工程 ,距地面 7 0m ,距拟建工程基底不足 2 0m ,影响了工程建设。设计人员通过对早期人防工程的加固处理 ,并在拟建工程设计中运用补偿基础理论 ,消除了地下早期人防工程的不良影响 ,使工程建设顺利进行。该工程实例对同类问题的解决具有实际意义。     

固定式塔吊基础设计与施工技术

潘家园小区工程建筑面积3．6万m^2，地下3层，地上28层。基槽标高-10.750m，地面至屋面女儿墙上口高度为83m，地上标准层层高2．8m。采用土钉墙护坡，坡度1：0．1，该工程的地质条件适宜桩基础的施工。     

某供用电综合楼地下基础底板堵漏防水技术

结合工程概况，分析了负二层地下基础底板渗漏的原因，介绍了防水堵漏方案的设计与优选以及主要施工工艺，实践证明，该工程堵漏防水技术可靠，效果良好，保证了工程的顺利进行。     

浅谈软弱地基应用干振法加固施工技术

分析了软弱地基干振法的加固原理及承载力特点，介绍了加固施工过程的程序及有关注意事项，对施工中的疑难问题提出了处理措施，总结了该施工技术的诸多优点。     

PTC预应力管桩在软土地基处理中的施工工艺

结合PTC预应力管桩在沪宁高速公路扩建工程中的应用，从路基底板的施工、PTC预应力管桩的施工方面对其施工工艺进行了介绍，总结了PTC预应力管桩在软土地基处理中的应用优点。     

不均匀地基上的基础设计

通过对在不均匀地基条件下的某教学楼基础设计的分析，确定了一种经济可行的基础形式，以解决不均匀地基条件的基础设计问题，进而为同类工程设计提供了借鉴。     

土钉支护在深基坑开挖中的应用

深圳某银行办公楼的深基坑支护采用了土钉支护，不但保证了施工质量，而且缩短了工期，减少了投资，以此工程为例介绍了土钉支护的土力学参数计算、结构设计、施工方案、技术措施和取得的显著效果。     

阳泉市常用地基处理方法探讨

对阳泉市常用地基处理方法进行了分析，从适用范围、设计要点、施工与检验等方面进行了论述，着重提出了阳泉市常用地基处理方法的特点与适用性。     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.