Construction projects are at risk of dispute. Dispute prevention is thus expected to avoid that cost. This paper aims to establish a model predicting the occurrence of disputes and identifying strategies regarding resource allocation in dispute avoidance. Factors in the model were identified by a literature review and conceptualised into aetiology and progression of dispute formation based on an analogy with epidemiological investigations. The model structure was established upon this analogy, validated against Bradford Hill criteria, and then quantified by conducting a Bayesian network analysis with samples returned from a questionnaire survey. The model shows high accuracy rates with both complete and incomplete input data. Finally, a sensitivity analysis was conducted to simulate the results of potential management measures available to be taken. This predictive model not only serves the prediction function but also traces back to factors causing disputes, and hence can be used to assist decision making before and during a construction process. At the centre of the model sits the causal mechanism investigation, but the existing methods only shed lights on correlational research without causal consideration. This paper devised an epidemiological investigation which covers from causal proposition to causal examination and contributes greatly to model establishment.

A pressure-grouted helical pile (PGHP) is a stiffened helical pile installed by using the simultaneous drilling and grouting technique. The formation of the soil-cement column surrounded by the helical pile is influenced by the multiple installation parameters, including helix number, helix size, and drilling speed. A series of small-scale model tests was carried out in clay to investigate the influence of installation parameters on axial behavior of PGHP and the load transfer mechanism. The model piles were pulled out after the loading tests, and the dimensions of the soil-cement columns were measured. The ultimate compressive bearing capacities of the PGHPs were 260% to 293% higher than the un-grouted helical piles. The ultimate bearing capacities of the PGHPs were proportional to the increase of the helix number and helix size and decreased with the drilling speed. The average bond diameter of the soil-cement column was 1.26 to 1.35 times the helix size. The adhesion between the soil-cement column and the surrounding clay was in the range of 0.8 to 1.2. The overall results examine the feasibility of the simultaneous drilling and grouting technique in clay and the improvement of the axial bearing performance compared to un-grouted helical piles.

The aim of this paper is to understand the formation mechanism of and establish a predictive model for payment issues faced by contractors in China’s construction industry. Factors critical to such issues were first identified under different categories from literature and verified by a Delphi survey. This paper identified critical factors under three categories, i.e., culture, client’s financial management, and interactions and processes. A focus group was then conducted to understand the roles played by each category of factors. Specifically, the cultural factors explained the origination of payment. By taking an analogy, the current unhealthy culture acted as pathogens of payment issues as a disease in the body of the construction industry; the financial management capability of the client acted as the defense system; and the interactive and process factors played the role as trigger and catalyst. It was followed by a logical deduction assisted by another Delphi survey to understand the associations among the identified factors. The model structure was hence constructed upon the factors and their associations and was then quantified by Bayesian belief network parameter learning with quantitative data collected from a questionnaire survey. As such, the formation mechanism of payment issues was investigated, based on which a Bayesian classifier was established from the perspective of Chinese contractors. The model demonstrated a high accuracy rate of more than 90%. This paper systematically investigated the formation mechanism of payment issues in China’s construction industry and revealed that root causes of payment issues could not be eliminated in a short term. The predictive model endows contractors with advantages in proactively evaluating if they are capable of tackling the risks of potential payment issues without disturbing the current power balance of the industry.

Purpose: The purpose of this research is to advise on UK construction delay strategies. Critical delay factors were identified and their interrelationships were explored; in addition, a predictive model was established upon the factors and interrelationships to calculate delay potentials.Design/methodology/approach: The critical causes were identified by a literature review, verified by an open-ended questionnaire survey and then analysed with 299 samples returned from structured questionnaire surveys. The model consisted of factors screened out by Pearson product–moment correlational coefficient, constructed by a logical reasoning process and then quantified by conducting Bayesian belief networks parameter learning.Findings: The technical aspect of construction project management was less critical while the managerial aspect became more emphasised. Project factors and client factors present relatively weak impact on construction delay, while contractor factors, contractual arrangement factors and distinctively interaction factors present relatively strong impact.Research limitations/implications: This research does not differentiate delay types, such as excusable vs non-excusable ones and compensable vs non-compensable ones. The model nodes have been tested to be critical to construction delay, but the model structure is mostly based on previous literature and logical deduction. Further research could be done to accommodate delay types and test the relationships.Originality/value: This research updates critical delay factor list for the UK construction projects, suggesting general rules for resource allocation concerning delay avoidance. Besides, this research establishes a predictive model, assisting delay avoidance strategies on a case-by-case basis.