Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Khalaf, Tarq Zaed; Çağlar, Hakan; Çağlar, Arzu; Hanoon, Ammar N.

doi:10.28991/cej-2020-03091478

Cited by 43 publications

(15 citation statements)

References 20 publications

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“…Khalaf et al [27] have applied PSO in estimating cost and duration of 60 construction projects at the early stage. What has been inferred from this study is that PSO has been well performed with high accurate results, while it is encountering parameters with a wide range of variability.…”

Section: Methodsmentioning

confidence: 99%

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

2020

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Construction cost predictions to reduce time risk assessment are indispensable steps for process of decision-making of managers. Machine learning techniques need adequate dataset size to model and forecast the cost of projects. Therefore, this paper presents analysis and studied manuscripts that proposed for cost estimation with machine learning techniques for the last 30 years. The impact of this manuscript is deep studied of machine learning techniques and applied an analysis methodology in cost estimation based on direct cost and indirect cost of construction projects, which consists of two parts. In the first part, for study the proposals, we focus on collecting related studied from Google Scholar and Science Direct journals. The interested application areas for project cost estimation are building, highway, public, roadway, waterrelated constructions, road tunnel, railway, hydropower, power plant and power projects. The second part is regarded to the analysis of the proposals. For cost analysis, there are possibilities to consider two approaches as qualitative and quantitative. However, reflect to the machine learning techniques the quantitative approach is studied. In quantitative approach, we categorized the models in three parts, as statistical, analogues and analytical model and analyse them based on their features. Correspondingly, papers have been thoroughly investigated based on the application area, method applied, techniques implemented, journals, which have been published in, and the year of publication. The most important outcome of this study is to find out the different analytics methods and machine learning algorithms to predict the cost estimation of construction and related projects and aid to find out the suitable applied methods.

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Section: Methodsmentioning

confidence: 99%

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

2020

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“…This cognitive part resembles the individual memory of the better place for the particle. The consequence of this term is that herds return to their best places, similar to the tendency for individuals to return to the most satisfying situations or places in the past [44,45].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

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Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.

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“…Machine learning techniques require adequate data set size to model and project costs [30]. Particularly, some machine learning techniques were adopted to estimate or predict costs of various project types such as building projects [31][32][33][34][35][36][37][38][39][40][41], highway projects [42][43][44][45][46][47][48], public projects [49][50][51][52][53], roadway projects [54][55][56][57][58][59], water-related construction projects [60][61][62], road tunnel projects [63,64], railway projects [65,66], hydropower projects [67,68], power plant and power projects [69].…”

Section: Cost Estimation and Predictionmentioning

confidence: 99%

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Van

Quoc

2021

JASTT

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Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

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Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Cited by 43 publications

References 20 publications

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

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