Ka Chi Lam

Demotivating factors influencing the productivity of civil engineering projects

Workers on civil engineering projects are frequently confronted with problems that could lead to demotivation. Demotivation is caused not simply by a lack of motivators but the existence of certain situations that cause dissatisfaction and discourage individuals from pursuing desired goals. Workers who are inadequately motivated tend to make only a minimal effort, therefore reducing overall productivity potential. It is believed that removing certain demotivators will increase motivation without necessitating the addition of motivators. This paper aims to improve worker productivity by identifying factors that are likely to induce the demotivation of workers. Predominant demotivators and their effects on the productivity of workers in civil engineering projects are identified through an empirical survey in Hong Kong. Time losses due to demotivation were found to be as much as 13.6 man-hours/week, with material availability, overcrowded work areas and rework being the most significant demotivators involved.

The application of the ant colony optimization algorithm to the construction site layout planning problem

A good site layout is vital to ensure the safety of the working environment, and for effective and efficient operations. Moreover, it minimizes travel distance, decreases materials handling, and avoids the obstruction of materials and plant movement. Based on studies in the manufacturing industry, the cost of materials handling could be reduced by 20–60% if an appropriate facility layout is adopted. In designing a site layout, a planner will first position the key facilities that influence the method and sequence of construction, and then assign the remaining facilities in the available space that is left over. This process is similar to the positioning of facilities in the ant colony optimization (ACO) algorithm. The general principle of the ACO algorithm is to assign facilities to a location one by one, and the occupied locations are deleted from the location scope in the next assignment. In the study, ACO algorithm is employed to resolve the construction site layout planning problem in a hypothetical medium‐sized construction project. By applying fuzzy reasoning and the entropy technique, the study calculates the closeness relationship between facilities, in which the optimal site layout is affected by the mutual interaction of facilities.

Understanding the effect of the learning–forgetting phenomenon to duration of projects construction

Abstract The construction time needed for an operation is assembled from the average unit production time plus the buffer time. The times required to produce different units in an operation are assumed to be identical. Moreover, the unit production time preceding and following an interruption is estimated to be identical in most projects. During claims or dispute resolutions, construction professionals reject the concept of an interruption of learning-curve phenomenon. This research explores the learning and forgetting phenomena that exist in repetitive construction operations, and their influence on project productivity. The model of a learning–forgetting phenomenon integrated using a line of balance technique is shown. Using this, the loss of productivity caused by the learning–forgetting phenomenon can be predicted. The model provides an alternative method in works, resources, and cash flow scheduling; therewith, a more realistic scheduling of construction operations, resources and cash flow is achieved.

Prediction of tender price index directional changes

A multivariate discriminant analysis is described, aimed at predicting the direction of changes in the Hong Kong tender price index by utilizing the patterns of change in eight leading economic indicators. Two discriminant functions are derived which distinguish between ‘upward’, ‘constant’ and ‘downward’ index trends with a high degree of success. The predictive power of the discriminant model is tested by means of a simulated ex post holdout sample of eight index values. By comparing the group centroids, seven of the cases are correctly classified. The hit rate of the ‘upward’ and ‘constant’ groups is 100%, while the ‘downward’ group has a hit rate of 75%, suggesting the ‘downward’ trend to be a more difficult movement to predict. Despite this, the overall predictive results are considerably better than those that would have occurred by chance alone.

A satisfying leadership behaviour model for design consultants

Abstract Design of construction project is a collective effort involving a team of specialists from different organisations. A lack of direct contractual relationships makes the line of authority subtle. The leadership of the design team leader could affect the productivity of the design team and therefore the project success. This paper aims to establish the relationship between leadership behaviours of design team leader and the satisfaction of the design team members. An empirical survey was carried out in Hong Kong and the results indicate that “charismatic” and “participative” leadership behaviours of the design team leader primarily determine the satisfaction of the team members. A satisfying leadership behaviours (SLB) model has been developed by multiple regression analysis based on the identified leadership behaviours. The SLB model was validated and the result reveals that the prediction error of the model is satisfactory. The practical usage of the SLB model has also been examined in this paper.

MBNQA‐oriented self‐assessment quality management system for contractors: fuzzy AHP approach

Many construction clients are not satisfied with the quality performance achieved on their projects though many contractors are ISO9000:1994 certified. Total quality management (TQM) has been a widely applied quality management system for obtaining the benefits of better quality and higher customer satisfaction through the spirit of continuous improvement, which is also adopted by ISO9001:2000 version. It is believed that TQM can help to raise quality and productivity in the construction industry. Self‐assessment systems provide an opportunity to design in quality on an organization‐wide basis, in which the self‐assessment process allows the organization to identify its strengths and weaknesses for continuous improvement actions. A MBNQA‐oriented self‐assessment quality management system (SQMS), which is based on the seven criteria of Malcolm Baldrige National Quality Award (MBNQA), for construction contractors to benchmark, is proposed. A questionnaire survey of Hong Kong construction quality management experts, in which a fuzzy analytical hierarchy process (AHP) was employed to calculate the weights of the seven criteria, was carried out. Remarkable differences for the allocation of weights in the seven criteria particularly in the input criteria (leadership, strategic planning and customer and market focus) and the ‘results’ criterion compared with the original weights of MBNQA were observed.

An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making

Most real world decision-making combines quantitative and qualitative (linguistic) variables. Conventional mathematics that combines qualitative and quantitative concepts exhibits difficulty in modelling actual problems. The research presented in this paper illustrates a mathematical approach to the solution of decision-making problems that combine qualitative and quantitative objectives. A methodical system for construction project management decision-making was developed using a combination of fuzzy multiple-objective decision-making theory and the fuzzy reasoning technique. The mathematical model can be applied to construction project management problems by suggesting an optimal path of corporate cash flow that results in the minimum use of resources. The information provided by the mathematical model allows the planner to eliminate excess use, or idleness, of resources during the construction of a project. Such information is indispensable for decision-makers in analysing the best time to invest in a new project. A case study is demonstrated to illustrate the application to a management decision problem.

The significance of financial risks in BOT procurement

This paper explores the significance of the financial risk characteristics of Build-Operate-Transfer (BOT) projects. The objective was to identify and discuss the significance of the types of financial risk variables in conjunction with the different phases of procurement. A survey was therefore conducted to investigate the nature of the relationships between the financial risk variables and the different phases of BOT projects. ‘Interest rate fluctuation’ was the most significant financial risk variable in the pre-investment phase. For the implementation phase, both the variables ‘design deficiency’ and ‘time overrun’ were found to be highly statistically significant. The variable ‘time overrun’ was found to be the most statistically significant in the construction phase. The majority of the risk variables were considered to be moderately significant in the operations phase; these included ‘competition’, ‘currency exchange restrictions’ and ‘defective products or facilities’. A mathematical model employi...

Efficacy of Using Support Vector Machine in a Contractor Prequalification Decision Model

Contractor prequalification is basically a nonlinear two-group classification problem. A robust contractor prequalification decision model should include the ability of handling both quantitative and qualitative data. Support vector machine (SVM) is a set of related supervised learning methods which can handle data in a high dimensional feature space for nonlinear separable problems. A new contractor prequalification decision model using SVM is proposed to assist clients to identify qualified contractors for tendering in this study. A case study was used to validate the proposed decision model and the classification ability was compared with neural networks (NNs) and principal component analysis (PCA). The results show that the proposed SVM model outperforms NN and PCA and the merits of using SVM to mitigate the limitations of using NN are elaborated. The proposed decision model is an ideal alternative for supporting clients to perform contractor prequalification decision making.

An Artificial Neural Network and Entropy Model for Residential Property Price Forecasting in Hong Kong

Summary Traditional approaches for housing price prediction fall short of accuracy, as it is difficult to identify a set of variables and account for their weightings when conducting forecasting. This study aims to explore an effective and efficient mathematical model for the housing price forecasting, so as to help developers, purchasers and financial institutes to obtain more reasonable property pricing through better decision‐making in the context of the fluctuant property market in Hong Kong. It began with a review of the macro and micro factors that affect the housing price and an entropy‐based rating and weighting model was presented with the aim of providing objectives and reasonable weighting to these variables. Then based on the key variables, the predictive ability of artificial neural networks (ANNs) was examined. In the empirical study, data were quantified and scaled with reasonable assumptions. Various networks were designed to examine the performance of ANN towards different parameters. Different sample sizes and different sets of input variables, together with different net structures and net types were undertaken to test the accuracy of ANN. From the comparison results of the R squared, as well as the mean absolute errors, the authors found that ANN performs well in forecasting with smaller sample size and standard net type. The overall results of this research demonstrated that the integration of Entropy and ANN can serve desirable function in the housing price forecasting progress.