David K. H. Chua

Key determinants for construction schedule performance

Abstract This study identifies key determinants for construction schedule performance from a list of factors related to the project manager, project team, planning and control efforts. Objective data on completed projects was analysed using neural network approach. Five key determinants identified are: (1) time devoted by the project manager to a specific project; (2) frequency of meetings between the project manager and other project personnel; (3) monetary incentives provided to the designer; (4) implementation of constructability program; and (5) project manager experience on projects with a similar scope. The importance of these key determinants is discussed as it relates to achieving specific construction schedule performance.

Case-Based Reasoning Approach to Construction Safety Hazard Identification: Adaptation and Utilization

Risk assessment, consisting of hazard identification and risk analysis, is an important process that can prevent costly incidents. However, due to operational pressures and lack of construction experience, risk assessments are frequently poorly conducted. In order to improve the quality of risk assessments in the construction industry, it is important to explore the use of artificial intelligence methods to ensure that the process is efficient and at the same time thorough. This paper describes the adaptation process of a case-based reasoning (CBR) approach for construction safety hazard identification. The CBR approach aims to utilize past knowledge in the form of past hazard identification and incident cases to improve the efficiency and quality of new hazard identification. The overall approach and retrieval mechanism are described in earlier papers. This paper is focused on the adaptation process for hazard identification. Using the proposed CBR approach, for a new work scenario (the input case), a most relevant hazard identification tree and a set of incident cases will be retrieved to facilitate hazard identification. However, not all information contained in these cases are relevant. Thus, less relevant information has to be pruned off and all the retrieved information has to be integrated into a hazard identification tree. The proposed adaptation is conducted in three steps: (1) pruning of the retrieved hazard identification tree; (2) pruning of the incident cases; and (3) insertion of incident cases into the hazard identification tree. The adaptation process is based on the calculation of similarity scores of indexes. A case study based on actual hazard identifications and incident cases is used to validate the feasibility of the proposed adaptation techniques.

Case-Based Reasoning for Construction Hazard Identification: Case Representation and Retrieval

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach.

Neural networks for construction project success

Abstract Being able to identify key attributes for successful project performance is of paramount importance to project owners, contractors, and designers. Understanding these key factors can help in the efficient execution of a construction project. This paper identifies key project management attributes associated with achieving successful budget performance using a neural network approach. Neural network models were developed using field data comprising potential determinants of construction project success. Altogether eight key project management factors were identified: (1) number of organizational levels between the project manager and craft workers; (2) amount of detailed design completed at the start of construction; (3) number of control meetings during the construction phase; (4) number of budget updates; (5) implementation of a constructability program; (6) team turnover; (7) amount of money expended on controlling the project; (8) the project managers technical experience. The final model, after sufficient training, can also be used as a predictive tool to forecast budget performance of a construction project. This approach allows the budget performance model to be built even though the functional interrelationships between inputs and output are not clearly defined. The model also performs reasonably well with incomplete information of the inputs.

Neural network analysis of construction safety management systems: a case study in Singapore

A neural network analysis was conducted on a quantitative occupational safety and health management system (OSHMS) audit with accident data obtained from the Singapore construction industry. The analysis is meant to investigate, through a case study, how neural network methodology can be used to understand the relationship between OSHMS elements and safety performance, and identify the critical OSHMS elements that have significant influence on the occurrence and severity of accidents in Singapore. Based on the analysis, the model may be used to predict the severity of accidents with adequate accuracy. More importantly, it was identified that the three most significant OSHMS elements in the case study are: incident investigation and analysis, emergency preparedness, and group meetings. The findings imply that learning from incidents, having well-prepared consequence mitigation strategies and open communication can reduce the severity and likelihood of accidents on construction worksites in Singapore. It was also demonstrated that a neural network approach is feasible for analysing empirical OSHMS data to derive meaningful insights on how to improve safety performance.

Predicting Change Propagation and Impact on Design Schedule Due to External Changes

Design projects often face changes from external sources causing redesign to many interdependent downstream activities. Depicting change propagation on the downstream activities and subsequent impact on design completion is a great challenge. This paper proposes a change propagation model to predict the change propagation on the downstream activities due to different degrees of change that might be initiated at different stages during a design project. The initiated change may cause change of different degrees to its immediate successors. The probability values of the change form a transition matrix for each dependency. The proposed model utilizes these transition matrices to depict the change propagation at the downstream. The change propagation model is then integrated with the scheduling model to schedule the propagated changes and to assess the overall impact on design completion and redesign (or loss in productivity). The effectiveness of the integrated model has been described with an illustrative case example. Such prediction and quantification of change impact would help project managers take the necessary actions for a proposed change.

Aggregate production planning for shipbuilding with variation-inventory trade-offs

Shipbuilding is a complex production system characterised by a complicated work and organisation structure, prolonged production lead time, and heterogeneous resource requirements. Thus, effectively planning all involved activities presents a challenging task and requires the timely coordination between the successive production stages at the plant level and effective resource allocation at the workshop level. With the work breakdown structure of all projects and their corresponding building strategies, the aggregate production planning (APP) is to address two important issues, namely, workforce level and inventory usage so that the fluctuating demands from downstream processes can be satisfied in a cost-effective manner. To achieve this, a novel APP model is proposed for ship production to minimise the variation of aggregate man-hour over the planning horizon and simultaneously minimise the logistic demands of the interim products. In view of the combinatorial nature and computational complexity, a directed genetic algorithm based solver has been developed to solve the two-conflicting-objective optimisation problem. The proposed approach has been applied to a case study and preliminary results have shown certain effectiveness in handling various situations with different planning strategies.

Collaborative scheduling over the internet

The modern global economy compels people to communicate, collaborate, and cooperate efficiently. To date, information technology has facilitated this communication and collaboration by making the transfer of information quicker and more efficient through the use of digital and computer communications. However, it is argued that the efficient transfer of information is not enough; people still must coordinate their decisions based on the information exchanged. There is always the danger that the volume of information and the pace at which the information is exchanged will overwhelm the people, and they will not realize the implications of their decisions based on this information. This is especially so when the information exchange is across organizational boundaries. Computer support for such decision coordination is still very much lacking. This article looks at the subject of decision coordination in a very task-specific area, that of precast fabrication scheduling. It discusses the issues involved and describes the proposed system model for implementing a groupware application for doing distributed scheduling. It also discusses the technology that will be used to implement the application.

Quantification of Spatial Temporal Congestion in Four-Dimensional Computer-Aided Design

This paper further develops the models proposed by prior research in the field of workspace conflict using four-dimensional computer-aided design. The approach developed here analyzes spatial demand and supply from the perspective of construction operators, and a modeling methodology based on spatiotemporal utilization is proposed. The utilization factor model is developed to show that the criticality of the operator’s spatiotemporal demand leads to worksite congestion and that congestion is a form of worksite conflict. The interference of other space entities increases the space demand, and this increment is quantified with a “dynamic space interference” index This indicator is developed to identify activity spaces which suffer congestion. A decision making tool, the “congestion penalty indicator,” is developed which obtains a schedule-level value for analysis, evaluation, and comparison. Finally, a case study on the refurbishment of an oil refinery column is used to demonstrate the application of the ab...

PDM++: Planning Framework from a Construction Requirements Perspective

Construction requirements represent the key preconditions for construction. These include topological precedence, key resources, space requirements, etc. Consequently, identifying them is necessary for feasible construction planning to be achieved. Despite this, little attention has been given to the impact of construction requirements on a project schedule, possibly because of the lack of a good tool for representing these requirements. This paper distinguishes construction requirements into static and dynamic types, according to changes in the need of the requirement during its life cycle. A modeling framework, PDM++, is then proposed. The framework deals with schedule constraints arising from both static and dynamic construction requirements, provides greater semantic expression to capture schedule constraints unambiguously, and facilitates the representation of interdependent conditional relationships. The concept of meta-intervals is also devised to represent complex requirements involving several activities and schedule constraints, and it facilitates modeling at higher levels of plan abstractions. Finally, an illustrative case study is presented to show the applicability of PDM++ in representing schedule constraints and alternative scheduling from a construction requirements perspective.