The impact of Engineering, Procurement and Construction (EPC) Phases on Project Performance: A Case of Large-scale Residential Construction Project

Selecting an Appropriate Configuration in a Construction Project Using a Hybrid Multiple Attribute Decision Making and Failure Analysis Methods

To successfully complete a project, selecting the most appropriate construction method and configuration is critical. There are, however, plenty of challenges associated with these complex decision-making processes. Clients require projects with the desired cost, time, and quality, so contractors should trade-off project goals through project configuration. To address this problem, in this study, an integrated FTA-DFMEA approach is proposed that implements the integrated AHP-TOPSIS method to improve construction project configuration. The proposed approach applies quality management techniques and MADM methods concurrently for the first time to improve construction project configuration considering project risks, costs and quality. At first, the Client’s requirements and market feedback are considered to identify potential failures in fulfilling project goals, and an integrated AHP-TOPSIS is used to select the most critical potential failure. Then fault tree analysis is used to indicate minimal paths. An inverse search in the operational model is performed to determine relevant tasks and identify defective project tasks based on WBS. Afterward, failure modes and effect analysis are applied to identify failure modes, and an integrated AHP-TOPSIS is used to rank failure modes and select the most critical one. Then Corrective actions are carried out for failure modes based on their priority, and project configuration is improved. This study considers construction resource suppliers with different policies, delivery lead times, warranty costs, and purchasing costs. Moreover, redundancy allocation and different configuration systems such as series and parallel are taken into account based on the arrangement and precedence of tasks. Finally, a case study of a building construction project is presented to test the viability of the proposed approach. The results indicate that the proposed approach is applicable as a time-efficient and powerful tool in the improvement of construction project configuration, which provides the optimal output by considering various criteria with respect to the client’s requirements and contractor’s obligations. Moreover, the algorithm provides various options for the contractor to improve the implementation of construction projects and better respond to challenges when fulfilling project goals.

Selecting Appropriate Risk Response Strategies Considering Utility Function and Budget Constraints: A Case Study of a Construction Company in Iran

Successful implementation of construction projects worldwide calls for a set of effective risk management plans in which uncertainties associated with risks and effective response strategies are addressed meticulously. Thus, this study aims to provide an optimization approach with which risk response strategies that maximize the utility function are selected. This selection is by opting for the most appropriate strategies with the highest impact on the project regarding the weight of each risk and budget constraints. Moreover, the risk assessment and response strategy of a construction project in Iran as a case study, based on the global standard of the project management body of knowledge (PMBOK) and related literature, is evaluated. To handle the complexity of the proposed model, different state of the art metaheuristic algorithms including the ant lion optimizer (ALO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO). These algorithms are validated by the exact solver from CPLEX software and compare with each other. One finding from this comparison is the high performance of MFO and HHO algorithms. Based on some sensitivity analyses, an extensive discussion is provided to suggest managerial insights for real-world construction projects.

Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

An Approach to the Analysis of Causes of Delays in Industrial Construction Projects through Planning and Statistical Computing

The analysis of the planning activities of industrial construction projects can help to evaluate some of the causes that have an impact on the variation of execution times and can also contribute to identifying those activities and components that are most likely to experience or cause delays. Data analysis is facilitated by the use of techniques based on statistical programs, allowing delays to be unequivocally linked to the different elements that make up these projects. In a theoretical study, a simulation is carried out with data that are hypothetical but consistent with real projects, which are transformed and standardized before being uploaded to the statistical software. Using the statistical software’s graphical interface, the data set is analyzed from a descriptive point of view, unraveling the relationships between variables and factors by means of contingency tables and scatter plots. Using other techniques such as the comparison of variables and correlation studies, as well as linear regression and variance analysis, the characteristics are evaluated and the differences in project delays are investigated in order to determine, after the fact, which components have the highest rates of delay in execution times.

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Purpose

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

Design/methodology/approach

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

Findings

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

Originality/value

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

Analysis of Contracts to Build Energy Infrastructures to Optimize the OPEX

The employer (owner) of the project wants to obtain the maximum profit for the money invested and the consultant (contractor) will try to give less for that money. The regulation of their relationship is based on the contractual agreement, which in the energy sector is mainly based on the engineering, procurement, and construction (EPC) model. The objective of this work was to evaluate which factors should be included in the drafting of contracts, to minimize problems between the parties, and thus minimize execution costs and optimize operation and maintenance costs. Information and data on the integration of operability and maintainability criteria in contracts for 158 projects, with a total contract value of close to €40,000M, were analyzed. Several of those projects corresponded to wind, solar, and hydroelectric plants. The information collected the perception of the agents involved, and was classified according to the experience of the agents consulted in the operation and maintenance areas. Finally, the proposed criteria were prioritized. In general, the owner is willing to introduce these criteria in his contracts if they reduce the operation and maintenance cost by around 1–5%, while the contractor is interested in increasing his probability to be selected by 1–3%.

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.

A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation

Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.

Competitive Capabilities for the Innovation and Performance of Spanish Construction Companies

This research analyses the influence of internal capabilities, identified as strategic by the literature, on the performance and innovation of Spanish construction companies during a recessionary period. Based on this, we studied whether innovative, marketing, financial, managerial, and human capabilities affect competitive success in terms of fostering innovation and the performance of firms. Empirical evidence is provided by performing survey research with a sample of 94 Spanish construction firms. The results show that firm innovation is fostered by innovative, financial, and human capabilities, and that performance is promoted by innovation, and financial and human capabilities. Human capabilities have the most important effect on both innovation and the performance of the company.