Minimizing delay in construction projects in Tehran, Iran

Construction industry is one of the most profitable sectors in Iran’s economic. Delay is common problem in the construction projects in Iran. This research, by considering the main causes of delay, prepared appropriate suggestions to minimize delay in construction projects in Tehran (Capital of Iran). Literature review provided up to date information with current literature and based on reviewing past publications. The questionnaires were distributed among respondents who are involved in the construction project in Tehran, Iran. The process of data analysis and discussions were conducted based on the questionnaire survey to identify the frequency of occurrence and severity affect of delay as well as findings the most effective methods of minimizing delay in Tehran’s construction projects. As a result of this study, respondents believe that ‘Delay in payment to contractor’ by client, ‘Difficulties in financing’ by contractor, ‘Slowness in decision making’ by client and ‘Poor site management’ by contractor are the most frequent and severe causes of delay in Tehran construction projects. Moreover the negative effects of delay on construction projects are: Cost Overrun, Change in Schedule and Liquidated Damage. Furthermore, the most effectiveness methods of minimizing delay are as follows: Pay progress payment to the contractor on time’ by client, ‘Accurate initial cost and time estimates’ by client and contractor, ‘Competent personnel of contractor / sub-contractor’ by contractor. Finally and based on the findings of this research, there were some recommendation to minimize the rate of delay in construction project in Tehran

Durability and leachability of concrete containing coal bottom ash and fly ash

Concrete is one of the main construction materials, though its application in construction has negative environmental impact such as carbon dioxide (CO2) emission and depletion of natural resources. In order to reduce the use of river sand as natural fine aggregate, the utilization of waste materials such as coal bottom ash (CBA), recycle concrete aggregate and recycle glass is quite effective. Moreover, in terms of reducing the amount of CO2 emission from cement industry, the utilization of supplementary cementitious materials (SCMs) such as coal fly ash (CFA), silica fume, blast-furnace slag and biomass ash is quite common and beneficial. In this study, the effect of using CBA as sand replacement and CFA as partial replacement of cement on various mix designs, was examined. The performance was investigated based on physical and mechanical properties, durability and leaching performance. The resistance to sulfuric acid and sulfate solutions and elevated temperature was also investigated. Two different leaching methods to obtain leaching performance of coal ash concrete performed were batch and tank leaching tests. Results revealed that concrete workability reduced with increasing percentage of CBA content replacing sand. At the early age of 28 days, no significant effect was observed in compressive, flexural and tensile strengths of all concrete samples. After curing ages of 91 and 180 days, compressive strength of both the experimental and control concrete samples increased significantly but remained almost similar. However, flexural and splitting tensile strengths of the experimental mix containing 75% CBA and 20% CFA exceeded more than the control sample. Moreover, drying-shrinkage of experimental concrete mixtures containing 50%, 75% and 100% CBA and 20% CFA was lower than the control mix. In general, the study revealed that coal ash concrete exhibited good influence in terms of resistance to chemical attacks than the control specimen. On the other hand, coal ash concrete mixtures showed higher loss in weight when exposed to higher temperature. Finally, from the results of leaching tests, it is observed that there is no leaching of any heavy metals. It is concluded that the experimental concrete mixes can be used which will minimize the use of natural resources, reducing energy and environmental problems to a great extent

A New Enhanced Hybrid Grey Wolf Optimizer (GWO) Combined with Elephant Herding Optimization (EHO) Algorithm for Engineering Optimization

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms

An Applied Study on Integration Edges of Failure and TOPSIS to Educational Environment Safety Assessment: A Case Study

A reliability and safety assessment for a bunch of listed schools can be challengeable for experts by the checklist system for safety reports. This paper aimed to respond to this challenge by merging the edges of failure 'EoF' and the technique for order of preference by similarity to ideal solution 'TOPSIS' to achieve an integrative approach for educational environment safety assessment. The qualitative assessment was implemented to detect safety faults in the case study area based on the results of the inspections. Then, the quantitative assessment was done to calculate critical points in edges of failure using the TOPSIS method. These points have been calculated for a bunch of listed schools that detected safety faults, and it also takes the form of the 'Jeopardous Pentagon' to calculate 'EoF Integration Mode'. It is an overall safety assessment to indicate performances region by region. This paper collected items of information about the twelve schools in Shahriar divided into three districts. Afterwards, a dangerous area is estimated to rank the existing options by the amount of achievement information. The first rank of the dangerous area between existence options is Shahriar two district. The most critical sides of the JP for first ranked reflect the human error 'RHE' and cultural governance 'CG' by values 0.989 and 0.989 for both intersection points. The combination of EoF and TOPSIS is recommended to apply for a physical and non-physical environment based on the safety checklist system

Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential construction safety articles were analyzed from a technical aspect, such as convolutional neural networks, recurrent neural networks, and general neural networks. In the context of safety management, three main research directions were identified: utilizing DL for behaviors, physical conditions, and management issues. Overall, applying DL can resolve important safety challenges with high reliability; therein the CNN-based method and behaviors were the most applied directions with percentages of 75% and 67%, respectively. Based on the review findings, three future opportunities aiming to address the corresponding limitations were proposed: expanding a comprehensive dataset, improving technical restrictions due to occlusions, and identifying individuals who performed unsafe behaviors. This review thus may allow the identification of key areas and future directions where further research efforts need to be made with priority

Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential construction safety articles were analyzed from a technical aspect, such as convolutional neural networks, recurrent neural networks, and general neural networks. In the context of safety management, three main research directions were identified: utilizing DL for behaviors, physical conditions, and management issues. Overall, applying DL can resolve important safety challenges with high reliability; therein the CNN-based method and behaviors were the most applied directions with percentages of 75% and 67%, respectively. Based on the review findings, three future opportunities aiming to address the corresponding limitations were proposed: expanding a comprehensive dataset, improving technical restrictions due to occlusions, and identifying individuals who performed unsafe behaviors. This review thus may allow the identification of key areas and future directions where further research efforts need to be made with priority

Determining the causes of delay by using factor analysis in Tehran’s construction projects

Construction industry is one of the most profitable sectors in Iran’s economic. Delay is common problem in the construction projects in Iran. By considering all viewpoints of the parties, this research identified the most effective and severe causes of delay in construction projects in the Capital of Iran, Tehran. Questionnaires were distributed among respondents who are involved in the construction project in Tehran. The process of data analysis and discussions were conducted based on the two statistical techniques namely descriptive analysis (RII) and factor analysis. Using factor analysis, most critical factors of Tehran’s construction delay were recognized as: (1) lack of commitment; (2) inefficient site management; (3) poor site coordination; (4) Complexity in heritage and legislation; (5) Lack of estimation skills and skilled workers; (6) Lack of communication between parties; (7) Improper planning; and (8) Lack of clarity in contract. These results are anticipated to be important contributions to construction projects in Tehran in controlling the time overruns in construction contracts

Sustainable clean pervious concrete pavement production incorporating palm oil fuel ash as cement replacement

The significant increase in global urban population and rapid growth of impervious urban surfaces result in erosion of stream channels, flooding, and damage to stormwater infrastructures. The aim of this research was to study pervious concrete pavement as a sustainable solution to control the stormwater at source, reducing heat island effect and enhancing safety of driving. The sustainability of pervious concrete can be increased and the carbon dioxide emissions reduced by replacing a huge amount of ordinary Portland cement with waste materials such as palm oil fuel ash. Palm oil fuel ash is a waste material obtained from the combustion of oil palm shells and fibers in palm oil industry to produce electricity, which caused environmental problems in countries such as Indonesia and Malaysia. This study presented experimental investigations to assess the substitution of control pervious concrete with palm oil fuel ash up to 40% (by mass) to produce sustainable and eco-friendly pervious concrete pavement. Density and void content of specimens were determined at fresh and hardened-state. Falling head permeability test was carried out to investigate the stormwater filtration capacity. Compressive and tensile strengths were conducted on pervious concrete specimens. Skid and abrasion resistances were also employed to evaluate the effects of palm oil fuel ash on safety of driving and surface durability of the pervious concrete pavement. The results showed that void content and water permeability of pervious concrete increased slightly with increasing palm oil fuel ash, while compressive and tensile strengths decreased. They satisfy the typical range for pervious concrete according to American Concrete Institute. A minor effect of palm oil fuel ash on the skid resistance was observed, increasing its substitution levels caused the abrasion resistance of pervious concrete mixtures to decrease. The heavy metal concentrations in leachates of the pervious concrete containing 40% palm oil fuel ash were significantly lower than recommended in the standard. The pervious concrete containing 20% palm oil fuel ash presented the most optimum mixture both technically and environmentally