A model for evaluating causes of wastes and lean implementation in construction projects

The wastes in construction projects such as wastes in materials, time, resources and achieving customer needs can be minimized using the new philosophy of Lean Construction (LC). This paper proposed a two-level model based on fuzzy logic technique for evaluating Causes of Wastes (CWs) and lean implementation in construction projects. The probability of occurrence and importance of CWs were two input parameters in level 01 of the model, whereas the output was the level of waste. On the other hand, level 02 of the model depended on using three input parameters which were: level of waste, controllability level for CWs and lean implementation level, while the output was the lean effect. Several linguistic variables and logical rules were used for relating inputs and outputs and new indices were introduced. The model was applied and validated for data collected in two countries: Egypt and Kingdom of Saudi Arabia (KSA). Results demonstrated that the expected lean effect is found with a positive correlation with various levels of wastes and can be improved by increasing controllability and lean implementation levels. Regarding the comparative study between the two countries, distinct disparities in lean effect were clarified. Most measured CWs indices were different in both countries while indices values in KSA were higher than in Egypt either in waste, controllability or implementation levels. The results presented an optimum arrangement to reach an effective new lean evaluation model that could be implemented for moving the traditional construction towards LC. Finally, the model can be applied easily in most countries to help decision makers in evaluating CWs and lean implementation in their construction projects

Key Adoption Factors for Collaborative Technologies and Barriers to Information Management in Construction Supply Chains: A System Dynamics Approach

Construction processes are complex and dynamic. Like its other components, the construction supply chain (CSC) involves multiple stakeholders requiring varying levels of information sharing. In addition, the intensity and diversity of information in CSCs require dexterous management. Studies reveal that information complexity can be reduced using collaborative technologies (CTs). However, the barriers to information management (IM) hinder the CTs’ adoption process and cause complexity in CSCs. This research identifies barriers to IM and factors affecting the adoption of CTs in developing countries. In order to understand and address complexity, the system dynamics (SD) approach is adopted in this study. The aim is to investigate if SD can reduce information complexity using CTs. Causal loop diagrams (CLDs) were developed to understand the relationship between the IM barriers and CT adoption factors. The SD model, when simulated, highlighted three main components, i.e., complexity, top management support, and trust and cooperation, among others, as factors affecting the adoption of CTs. Addressing these factors will reduce information complexity and result in better IM in construction projects

Effects of Steel Fibers (SF) and Ground Granulated Blast Furnace Slag (GGBS) on Recycled Aggregate Concrete

[EN] Recycled aggregate is a good option to be used in concrete production as a coarse aggregate that results in environmental benefits as well as sustainable development. However, recycled aggregate causes a reduction in the mechanical and durability performance of concrete. On the other hand, the removal of industrial waste would be considerably decreased if it could be incorporated into concrete production. One of these possibilities is the substitution of the cement by slag, which enhances the concrete poor properties of recycled aggregate concrete as well as provides a decrease in cement consumption, reducing carbon dioxide production, while resolving a waste management challenge. Furthermore, steel fiber was also added to enhance the tensile capacity of recycled aggregate concrete. The main goal of this study was to investigate the characteristics of concrete using ground granulated blast-furnace slag (GGBS) as a binding material on recycled aggregate fibers reinforced concrete (RAFRC). Mechanical performance was assessed through compressive strength and split tensile strength, while durability aspects were studied through water absorption, acid resistance, and dry shrinkage. The results detected from the different experiments depict that, at an optimum dose (40% RCA, 20%GGBS, and 2.0%), compressive and split tensile strength were 39% and 120% more than the reference concrete, respectively. Furthermore, acid resistance at the optimum dose was 36% more than the reference concrete. Furthermore, decreased water absorption and dry shrinkage cracks were observed with the substitution of GGBS into RAFRC.SIThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a group research program under grant number RGP. 2/129/42 and Taif University Researchers Supporting Project grant number [TURSP-2020/324]

Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach

This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the individual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established

Key factors for implementation of total quality management in construction Sector: A system dynamics approach

Maintaining quality in construction projects is paramount to project success, achieved through techniques such as Total Quality Management (TQM). However, the key factors of TQM implementation in the construction industry of developing countries are not well explored. Accordingly, this paper evaluated the causative relationship and intricacies of TQM implementation in the construction sector of developing countries. A total of 28 key factors of TQM were captured through a literature review. Thereafter, 12 significant key factors were shortlisted. Lack of top management commitment, poor customer/client satisfaction, inadequate quality of education regarding TQM, and ineffective organizational quality culture emerged as impediments to implementing TQM in the construction sector. A Causal Loop Diagram (CLD) was developed to represent interrelations between the 12 shortlisted factors. In addition, a system dynamics model (SDM) was developed. The simulation results of the developed SDM indicated an increase in TQM implementation over the period under the defined system

Eco-friendly incorporation of crumb rubber and waste bagasse ash in bituminous concrete mix

The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix

Qualitative Analysis of Risks Affecting the Delivery of Land Surveying Project Activities

Land surveying projects (LSPs) suffer from the effects of many risk factors on the time and accuracy of these projects. Using field surveys, the main objective of this study was identifying the major activities and risk factors associated with LSPs’ execution, as well as assessing and analyzing the effects of the risk factors on the LSPs’ time and accuracy. Furthermore, the study aimed to classify and determine the responsibility of each risk factor and evaluate the responsibilities. Four main activities were categorized and presented, including reconnaissance works, planning works, data collection works, and data adjustment works. Moreover, forty-three risk factors that control the main activities and affect the time and accuracy of LSPs were recognized. The probabilities of occurrences for the risk factors and impacts on the time and accuracy of LSPs were determined as well as their combined effects. Key risk factors that had high threats on LSPs and affect time and accuracy were highlighted as the most critical risk factors. Many correlations were determined among risk factors affecting LSPs’ activity groups and their various effects on time and accuracy. The responsibilities of the surveying crew (chief, surveyor, assistance, office engineer) for each risk factors were correspondingly defined. The results showed that “Data collection works” is considered the riskiest activity group in LSPs and most of the key risk factors belonged to this group. Around 25% of the LSPs face time overrun and do not meet the required specifications. On the other hand, the surveyor was found to be responsible for most of the risk factors and the office engineer was signified by the lowest responsibility, while the responsibilities for most risk factors were single responsibility and few were shared by only dual responsibility

Qualitative Analysis of Risks Affecting the Delivery of Land Surveying Project Activities

Land surveying projects (LSPs) suffer from the effects of many risk factors on the time and accuracy of these projects. Using field surveys, the main objective of this study was identifying the major activities and risk factors associated with LSPs’ execution, as well as assessing and analyzing the effects of the risk factors on the LSPs’ time and accuracy. Furthermore, the study aimed to classify and determine the responsibility of each risk factor and evaluate the responsibilities. Four main activities were categorized and presented, including reconnaissance works, planning works, data collection works, and data adjustment works. Moreover, forty-three risk factors that control the main activities and affect the time and accuracy of LSPs were recognized. The probabilities of occurrences for the risk factors and impacts on the time and accuracy of LSPs were determined as well as their combined effects. Key risk factors that had high threats on LSPs and affect time and accuracy were highlighted as the most critical risk factors. Many correlations were determined among risk factors affecting LSPs’ activity groups and their various effects on time and accuracy. The responsibilities of the surveying crew (chief, surveyor, assistance, office engineer) for each risk factors were correspondingly defined. The results showed that “Data collection works” is considered the riskiest activity group in LSPs and most of the key risk factors belonged to this group. Around 25% of the LSPs face time overrun and do not meet the required specifications. On the other hand, the surveyor was found to be responsible for most of the risk factors and the office engineer was signified by the lowest responsibility, while the responsibilities for most risk factors were single responsibility and few were shared by only dual responsibility