Muhamad Azry Khoiry

Investigating Delays in Libyan Road Construction Projects Using Structural Equation Modelling (SEM)

Transportation infrastructure, specifically road projects, is the backbone of economic and social development of many countries. Successful road infrastructure projects are delivered with reduced cost, on time. Factors contributing to Construction delay in road construction projects in Libya were identified and ranked through a questionnaire survey distributed to owners, consultants, and contractors involved in road projects. A total of 256 completed questionnaire forms were received and analysed. A Structural Equation Modelling SEM Path Model of relationship between delay factors and effects in road construction was formulated and evaluated using [SEM] 21 software. 49 factors classified into eight groups of factors and three groups of effects of delay. The contractor group in delay factors had the greatest impact on road construction delay with path coefficient β-values of 0.249, while financial groups in delay effects had the greatest impact on road construction delay with path coefficient β-values of 0.88. The R2 value of the model is 0.48, indicating that the developed model substantially explains Construction delay. This rigorous multivariate analysis has identified several causative factors that contribute to delay in road construction projects in Libya. The findings will help all parties involved in construction projects to achieve better control over construction delays, and will provide support for practitioners to incorporate risk analysis for potential Construction delay in future projects. As well as for researchers in the field of road construction and understanding of the factors causing project cost overruns in developing countries.

Physical Damages Effect on Residential Houses Caused by the Earthquake at Ranau, Sabah Malaysia

Earthquake, the destructive natural disaster had recently stormed East Malaysia. This study aims to identify the physical effects of the earthquake to the building that occurred in Sabah, Malaysia. A survey method had been conducted among 221 citizens in the affected area to meet the requirements of this research objective. The result shows that 68% responded that building cracks had formed on the wall, 48% cracked floor, 23% cracked columns, 10% damages on roof and 8% responded no damages at all while only 2% stated the total collapse of the houses’ structures. Researchers have also identified that the impact of the earthquake towards their house yards shows that 55% and 12% responded experienced cracks on ground and landslide respectively, 25% with flood occurrence and 1% are caught with fire. Finally, almost 90% of the respondents are ready to upgrade their house structures. Thus, this research will be continued by developing the retrofitting and strengthening methods for the low rise buildings.

A Study of Identifying Significant Variables of Delays in Road Construction Via Structural Equation Modelling (SEM)

Delays have always been a major concern in road construction projects throughout the world, with significant financial and social impacts for stakeholders, and the occurrence of schedule delays has a serious impact on project investment, efficiency, cost, and reputation. Libya is one of the countries faces all these delay issues. This study explored variables of delays in road construction via Structural Equation modelling SEM. Drawing upon an earlier studies, combined with interviews of road experts. Confirmatory factor analysis CFA was applied to extract significant variables based on the questionnaire results to verify the relationships between the significant variables [8 Factors and 3 Effects] identified in road construction. Eleven variables were found to have significant impacts on delays in road construction. The results of the goodness of fit [GOF] showed that chi square is significant at 0.000 levels. Overall, Based on the CFI, TLI, and IFI indices with values more than the cut off value of 0.9 the model had good fit of data. Further. The root-mean-square error of approximation [RMSEA] was 0.033 which was within the perfect fit range. Additionally. The Relative CMIN/df [1.276] was less than 5 showed the good fit of the model and it was found that the unstandardized regression weights were all significant by the critical ratio test [> ±1.96, P < 0.001]. So the study has shown that CFA can quantify comprehensive relationships among a broad range of variables and contribute to resolving problems commonly experienced in the road construction industry.