Research

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25 pages, 1327 KiB

Open AccessArticle

Exploring Perceptions of the Adoption of Prefabricated Construction Technology in Pakistan Using the Technology Acceptance Model

by Muhammad Hamza, Rai Waqas Azfar, Khwaja Mateen Mazher, Basel Sultan, Ahsen Maqsoom, Shabir Hussain Khahro and Zubair Ahmed Memon

Sustainability 2023, 15(10), 8281; https://doi.org/10.3390/su15108281 - 19 May 2023

Cited by 1 | Viewed by 2083

Abstract

Prefabricated construction is being pursued globally as a critically important sustainable construction technology. Prefabricated construction technology (PCT) provides opportunities to effectively manage construction waste and offers venues to address the poor productivity and lackluster performance of construction projects, which are often expected to [...] Read more.

Prefabricated construction is being pursued globally as a critically important sustainable construction technology. Prefabricated construction technology (PCT) provides opportunities to effectively manage construction waste and offers venues to address the poor productivity and lackluster performance of construction projects, which are often expected to miss their budget and schedule constraints. Despite the significant benefits inherent in the adoption of PCT, research has shown an unimpressive exploitation of this technology in the building sector. A modified version of the popular technology acceptance model (TAM) was used to understand Pakistan’s building construction industry stakeholder’s acceptance of PCT and the factors that influence its usage. Data were collected from 250 building construction experts in the industry to test the hypotheses derived from the proposed model. Data analysis using covariance-based structural equation modeling revealed that construction industry stakeholders’ perceptions of perceived ease-of-use, perceived usefulness, trust, and satisfaction all strongly influenced PCT acceptance behavior. Moreover, results also confirmed the total direct and indirect effects of the perceived usefulness and perceived ease-of-use of behavioral intention toward using PCT, with trust and user satisfaction as mediators. The results of this research are expected to serve as a guide for the construction industry stakeholders to effectively plan, strategize, encourage, and increase the adoption of PCT to achieve sustainable construction outcomes in the building construction sector. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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18 pages, 962 KiB

Open AccessArticle

Error Management Climate and Job Stress in Project-Based Organizations: An Empirical Evidence from Pakistani Aircraft Manufacturing Industry

by Hassan Ashraf, Ahsen Maqsoom, Tayyab Tahir Jajja, Rana Faisal Tufail, Rashid Farooq and Muhammad Atiq Ur Rehman Tariq

Sustainability 2022, 14(24), 17022; https://doi.org/10.3390/su142417022 - 19 Dec 2022

Viewed by 1544

Abstract

Drawing on the JD-R model, this study examines the influence of error management climate (EMC) on the job stress of frontline aeronautical employees. It also analyzes the moderating role of psychological capital (PsyCap) dimensions (i.e., hope, optimism, self-efficacy, and resilience) for the relationship [...] Read more.

Drawing on the JD-R model, this study examines the influence of error management climate (EMC) on the job stress of frontline aeronautical employees. It also analyzes the moderating role of psychological capital (PsyCap) dimensions (i.e., hope, optimism, self-efficacy, and resilience) for the relationship between error management climate and job stress. The data was collected from 208 individuals through a questionnaire survey and was analyzed using a partial least squares structural equation modeling (PLS-SEM) approach. The results revealed that employees’ perceptions of error management climate have a significant negative impact on job stress. PsyCap optimism and PsyCap self-efficacy were found to have a negative moderating influence on the relationship between EMC and job stress. The other two dimensions of hope and resilience were found to have a moderating influence in the same direction as expected, but not at statistically significant levels. The findings of this study provide a unique perspective in realizing the part national and organizational cultures could play in either enhancing or attenuating the influence of an individual’s psychological resources such as psychological capital. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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22 pages, 3784 KiB

Open AccessArticle

Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan

by Tahir Ali Akbar, Azka Javed, Siddique Ullah, Waheed Ullah, Arshid Pervez, Raza Ali Akbar, Muhammad Faisal Javed, Abdullah Mohamed and Abdeliazim Mustafa Mohamed

Sustainability 2022, 14(21), 14572; https://doi.org/10.3390/su142114572 - 05 Nov 2022

Cited by 6 | Viewed by 2560

Abstract

Drinking water quality is a major problem in Pakistan, especially in the Abbottabad region of Pakistan. The main objective of this study was to use a Principal Component Analysis (PCA) and integrated Geographic Information System (GIS)-based statistical model to estimate the spatial distribution [...] Read more.

Drinking water quality is a major problem in Pakistan, especially in the Abbottabad region of Pakistan. The main objective of this study was to use a Principal Component Analysis (PCA) and integrated Geographic Information System (GIS)-based statistical model to estimate the spatial distribution of exceedance levels of groundwater quality parameters and related health risks for two union councils (Mirpur and Jhangi) located in Abbottabad, Pakistan. A field survey was conducted, and samples were collected from 41 sites to analyze the groundwater quality parameters. The data collection includes the data for 15 water quality parameters. The Global Positioning System (GPS) Essentials application was used to obtain the geographical coordinates of sampling locations in the study area. The GPS Essentials is an android-based GPS application commonly used for collection of geographic coordinates. After sampling, the laboratory analyses were performed to evaluate groundwater quality parameters. PCA was applied to the results, and the exceedance values were calculated by subtracting them from the World Health Organization (WHO) standard parameter values. The nine groundwater quality parameters such as Arsenic (As), Lead (Pb), Mercury (Hg), Cadmium (Cd), Iron (Fe), Dissolved Oxygen (DO), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Colony Forming Unit (CFU) exceeded the WHO threshold. The highly exceeded parameters, i.e., As, Pb, Hg, Cd, and CFU, were selected for GIS-based modeling. The Inverse Distance Weighting (IDW) technique was used to model the exceedance values. The PCA produced five Principal Components (PCs) with a cumulative variance of 76%. PC-1 might be the indicator of health risks related to CFU, Hg, and Cd. PC-2 could be the sign of natural pollution. PC-3 might be the indicator of health risks due to As. PC-4 and PC-5 might be indicators of natural processes. GIS modeling revealed that As, Pb, Cd, CFU, and Hg exceeded levels 3, 4, and 5 in both union councils. Therefore, there could be greater risk for exposure to diseases such as cholera, typhoid, dysentery, hepatitis, giardiasis, cryptosporidiosis, and guinea worm infection. The combination of laboratory analysis with GIS and statistical techniques provided new dimensions of modeling research for analyzing groundwater and health risks. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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22 pages, 4648 KiB

Open AccessArticle

An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM

by Daniela Durand, Jose Aguilar and Maria D. R-Moreno

Sustainability 2022, 14(20), 13358; https://doi.org/10.3390/su142013358 - 17 Oct 2022

Cited by 10 | Viewed by 1704

Abstract

This work explores the process of predicting energy consumption in smart buildings based on the consumption of devices and appliances. Particularly, this work studies the process of data analysis and generation of prediction models of energy consumption in Smart Buildings. Specifically, this article [...] Read more.

This work explores the process of predicting energy consumption in smart buildings based on the consumption of devices and appliances. Particularly, this work studies the process of data analysis and generation of prediction models of energy consumption in Smart Buildings. Specifically, this article defines a feature engineering approach to analyze the energy consumption variables of buildings. Thus, it presents a detailed analysis of the process to build prediction models based on time series, using real energy consumption data. According to this approach, the relationships between variables are analyzed, thanks to techniques such as Pearson and Spearman correlations and Multiple Linear Regression models. From the results obtained with these, an extraction of characteristics is carried out with the Principal Component Analysis (PCA) technique. On the other hand, the relationship of each variable with itself over time is analyzed, with techniques such as autocorrelation (simple and partial), and Autoregressive Integrated Moving Average (ARIMA) models, which help to determine the time window to generate prediction models. Finally, prediction models are generated using the Long Short-Term Memory (LSTM) neural network technique, taking into account that we are working with time series. This technique is useful for generating predictive models due to its architecture and long-term memory, which allow it to handle time series very well. The generation of prediction models is organized into three groups, differentiated by the variables that are considered as descriptors in each of them. Evaluation metrics, RMSE, MAPE, and R² are used. Finally, the results of LSTM are compared with other techniques in different datasets. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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19 pages, 4334 KiB

Open AccessFeature PaperArticle

Key Enablers of Resilient and Sustainable Construction Supply Chains: A Systems Thinking Approach

by Maria Ghufran, Khurram Iqbal Ahmad Khan, Fahim Ullah, Wesam Salah Alaloul and Muhammad Ali Musarat

Sustainability 2022, 14(19), 11815; https://doi.org/10.3390/su141911815 - 20 Sep 2022

Cited by 4 | Viewed by 2664

Abstract

In the globalized world, one significant challenge for organizations is minimizing risk by building resilient supply chains (SCs). This is important to achieve a competitive advantage in an unpredictable and ever-changing environment. However, the key enablers of such resilient and sustainable supply chain [...] Read more.

In the globalized world, one significant challenge for organizations is minimizing risk by building resilient supply chains (SCs). This is important to achieve a competitive advantage in an unpredictable and ever-changing environment. However, the key enablers of such resilient and sustainable supply chain management are less explored in construction projects. Therefore, the present research aims to determine the causality among the crucial drivers of resilient and sustainable supply chain management (RSSCM) in construction projects. Based on the literature review, 12 enablers of RSSCM were shortlisted. Using the systems thinking (ST) approach, this article portrays the interrelation between the 12 shortlisted resilience enablers crucial for sustainability in construction projects. The causality and interrelationships among identified enablers in the developed causal loop diagram (CLD) show their dynamic interactions and impacts within the RSSCM system. Based on the results of this study, agility, information sharing, strategic risk planning, corporate social responsibility, and visibility are the key enablers for the RSSCM. The findings of this research will enable the construction managers to compare different SCs while understanding how supply chain characteristics increase or decrease the durability and ultimately affect the exposure to risk in the construction SCs. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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17 pages, 3164 KiB

Open AccessArticle

Modified Activated Carbon Synthesized from Oil Palm Leaves Waste as a Novel Green Adsorbent for Chemical Oxygen Demand in Produced Water

by Hifsa Khurshid, Muhammad Raza Ul Mustafa and Mohamed Hasnain Isa

Sustainability 2022, 14(4), 1986; https://doi.org/10.3390/su14041986 - 10 Feb 2022

Cited by 6 | Viewed by 3243

Abstract

Palm tree waste is one of the most widespread forms of agricultural waste, particularly in areas where oil palms are cultivated, and its management is one of the industry’s key concerns. To deal with this palm waste, researchers are working hard to work [...] Read more.

Palm tree waste is one of the most widespread forms of agricultural waste, particularly in areas where oil palms are cultivated, and its management is one of the industry’s key concerns. To deal with this palm waste, researchers are working hard to work out the ways to convert this plentiful waste into useful material for future beneficial applications. The objective of this study was to employ chemical activation techniques to prepare a new activated carbon (AC) using discarded oil palm leaves (OPL) in Malaysia. Three chemical agents (H₃PO₄, NaOH and ZnCl₂), as well as three pyrolysis temperatures (400 °C, 600 °C and 800 °C) and various impregnation ratios (1:0.5–1:3) were used to optimize the preparation process. As a result, the oil palm leaves activated carbon (OPLAC), with prominent surface properties, was obtained by ZnCl₂ activations with a 1:1 impregnation ratio and carbonized at a pyrolysis temperature of 800 °C. The OPLAC-ZC had a surface area of 331.153 m²/g, pore size of 2.494 nm and carbon content of 81.2%. Results showed that the OPLAC-ZC was able to quickly (90 min) remove the chemical oxygen demand (COD) from produced water (PW), through chemical adsorption and an intraparticle diffusion mechanism. The material followed pseudo-second order kinetic and Freundlich isotherm models. The maximum adsorption capacity of organic pollutants forming COD in PW was found to be 4.62 mg/g (59.6 ± 5%). When compared to previous studies, the OPLAC-ZC showed equivalent or better COD removal capability. It is the first detailed study reporting the preparation of AC from OPL and applying it for organic pollutants adsorption forming COD in PW. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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20 pages, 6254 KiB

Open AccessArticle

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

by Abdus Samad Azad, Rajalingam Sokkalingam, Hanita Daud, Sajal Kumar Adhikary, Hifsa Khurshid, Siti Nur Athirah Mazlan and Muhammad Babar Ali Rabbani

Sustainability 2022, 14(3), 1843; https://doi.org/10.3390/su14031843 - 05 Feb 2022

Cited by 35 | Viewed by 3618

Abstract

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also [...] Read more.

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)₁₂ but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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Review

Jump to: Research

24 pages, 1923 KiB

Open AccessReview

A State of Review on Instigating Resources and Technological Sustainable Approaches in Green Construction

by Dhanasingh Sivalinga Vijayan, Parthiban Devarajan, Arvindan Sivasuriyan, Anna Stefańska, Eugeniusz Koda, Aleksandra Jakimiuk, Magdalena Daria Vaverková, Jan Winkler, Carlos C. Duarte and Nuno D. Corticos

Sustainability 2023, 15(8), 6751; https://doi.org/10.3390/su15086751 - 17 Apr 2023

Cited by 2 | Viewed by 2366

Abstract

Green building is a way to reduce the impact of the building stock on the environment, society, and economy. Despite the significance of a systematic review for the upcoming project, few studies have been conducted. Studies within the eco-friendly construction scope have been [...] Read more.

Green building is a way to reduce the impact of the building stock on the environment, society, and economy. Despite the significance of a systematic review for the upcoming project, few studies have been conducted. Studies within the eco-friendly construction scope have been boosted in the past few decades. The present review study intends to critically analyse the available literature on green buildings by identifying the prevalent research approaches and themes. Among these recurring issues are the definition and scope of green buildings, the quantification of green buildings’ advantages over conventional ones, and several green building production strategies. The study concludes that the available research focuses mainly on the environmental side of green buildings. In contrast, other crucial points of green building sustainability, such as social impacts, are often neglected. Future research objectives include the effects of climate on the effectiveness of green building assessment methods; verification of the actual performance of green buildings; specific demographic requirements; and future-proofing. Full article

(This article belongs to the Special Issue Sustainable Construction of Future: Opportunities and Challenges for Green and Buildings)

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