Hanbin Luo

Feedforward Analysis for Shield-Ground System

AbstractGround surface settlement is an important measurement in identifying potential damages for shield tunneling. Identifying the relationship between shield parameters and the resulting settlement is of vital importance to the reasonable adjustment of the shield parameters so as to control settlement development. However, many other factors, besides the shield parameters, affect settlement, which makes shield-ground interaction complicated. Therefore, a better method is necessary for extracting the shield-ground relationship for the purpose of steering shield tunneling. This paper proposes a method that incorporates smooth relevance vector machine (sRVM) and particle swarm optimization (PSO) for shield steering with concern for settlement control. First, smooth relevance vector machine with adaptive Gaussian kernel function is used to establish the relationship between the identified factors and the settlement. Particle swarm optimization is then applied to identify the appropriate kernel parameters. ...

Probabilistic risk assessment of tunneling-induced damage to existing properties

There is an intrinsic risk associated with tunnel construction, particularly in urban areas where a number of third party persons and properties are involved. Due to the limited availability of data for accidents and the complexity associated with their causation, it is therefore necessary to combine available historical data and expert judgment to consider all relevant factors to undertake a realistic risk analysis. Thus, this paper presents a hybrid approach that can be used to undertake a probabilistic risk assessment of the risks associated with tunneling and its likelihood to damage to existing properties using the techniques of Bayesian Networks (BN) and a Relevance Vector Machine (RVM). A causal framework that integrates the techniques is also proposed to facilitate the development of the proposed model. The developed risk model is applied to a real tunnel construction project in Wuhan, China. The results derived from the project demonstrated the models ability to accurately assess risks during tunneling, specifically the identification of accident scenarios and the quantification of the probability and severity of possible accidents. The potential of this risk model to be used as a decision-making support tool was also explored.

Predicting Safety Risks in Deep Foundation Pits in Subway Infrastructure Projects: Support Vector Machine Approach

AbstractAccurately predicting risks during the construction of deep foundation pits is pivotal to ensuring the safety of the workforce of public and adjacent structures. Existing methods for assess...

A BIM-based Code Compliance Checking Process of Deep Foundation Construction Plans

Deep foundation construction projects are quite risky, so it has to be checked by supervisors for code compliance and reviewed by a committee of experts for risk identification before the construction begins. The quality of code compliance checking directly affects the safety of deep foundation construction. Because of the limitations of manual checking by even the best supervisors, the goal of this research is to achieve an approach of BIM-based code compliance checking for deep foundation construction presented in BIM models, as well as a system based on the proposed approach. This research abides by the principles of four stages in rule-based system, and emphasizes the development of the library of checking knowledge and the standard of required information which are the key problems in achieving the BIM-based code compliance checking. Decision tables, production representation and a developing process similar to IDM development are used to solve the problems. Furthermore, a case study is provided to investigate the effect of the BIM-based code compliance checking process. It is proved that the BIM-based checking improves the efficiency and precision in code compliance checking and facilitates the risk identification at the next stage. It ensures the safety of deep foundation and reduces personal injury and property loss.

Development of a Construction Quality Supervision Collaboration System Based on a SaaS Private Cloud

In this paper, the background of the SaaS private cloud-based Construction Quality Supervision Collaboration System (CQSCS) is introduced; the current status and shortcomings of construction quality supervision are described, and methods are proposed to incorporate the novel software architecture and application models of the SaaS private cloud to strengthen construction quality supervision and management. The design and construction of the overall framework of the CQSCS were completed in the study; system functions and structure were designed and developed, such as the quality supervision unit, quality inspection unit and quality acceptance unit, whose targets are to serve construction quality supervision agencies, quality inspection agencies and units involved in on-site construction. Additionally, the new system is compared against the old system. Finally, based on the CQSCS used in Wuhan, the function system and applications of the construction quality supervision and management system are discussed, and the prospect of the wide-spread application of the designed system is speculated.

Ontology-Based Approach for Automated Quality Compliance Checking against Regulation in Metro Construction Project

A metro construction project has to meet the requirements of related standards and regulations. This is usually achieved through compliance checking. However, nowadays, as a manual and highly labour-intensive task, the construction quality compliance checking leads to excessive work for inspectors. Therefore, this paper proposes an ontological methodology to support automatic construction quality compliance checking, in which knowledge of regulation constraints is formalized in OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) and the construction quality inspection data are represented as instances of OWL ontology. The proposed methodology is illustrated with a regulation example taken from “Acceptance Codes for Construction Quality of Building Foundation (GB50202-2002)”.

Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach

Abstract Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. Such research has tended to focus on the use of computer vision and pattern recognition approaches that are overly reliant on the manual extraction of features and small datasets (

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.

Topological mapping and assessment of multiple settlement time series in deep excavation: A complex network perspective

Abstract This study proposed a novel methodology that integrates complex network theory and multiple time series to enhance the systematic understanding of the daily settlement behavior in deep excavation. The original time series of ground surface, surrounding buildings, and structure settlement instrumentation data over an excavation time period were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. The reconstructed settlement network has nodes corresponding to multiple settlement time series individually and edges regarded as nonlinear relationships between them. A deep excavation case study of the metro station project in the Wuhan Metro network, China, was applied to validate the feasibility and potential value of the proposed approach. Results of the topological analysis corroborate a small-world phenomenon with highly compacted interactions and provide the assessment of the significance among multiple settlement time series. This approach, which provides a new way to assess the safety monitoring data in underground construction, can be implemented as a tool for extracting macro- and micro-level decision information from multiple settlement time series in deep excavation from complex system perspectives.

Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data

Abstract This study proposes a risk analysis model based on the Bayesian network for diaphragm wall deflection to ensure safety during excavation. Compared with the traditional methods, such as fault tree analysis, the proposed approach can more accurately illustrate the dynamic and updated features of hydrogeological design and construction variables as construction progresses on the basis of metro construction field data, which can address the risks in uncertain environments and achieve dynamic control during the entire construction process. The procedures include risk identification, sample collection, model building and validation, risk analysis and reasoning, and real-time control measures. The proposed model was applied in the metro station construction project, in Wuhan Metro Line 7. Results showed that the proposed risk analysis method can achieve real-time risk analysis, which includes pre-accident, during-construction, and post-accident control, to ensure construction safety at the station.