Construction tender price estimation standardization (TPES) in Malaysia: modeling using fuzzy neural network

Purpose: The pre-tender estimation process is still a hazy and inaccurate process, despite it has been practiced over decades, especially in Malaysia. The methods evolved over time largely depend on the amount of information available at the time of estimation. More often than not, the estimate produced during the pre-tender stage is far more than the tender cost of the project and sometimes, it is perilously underestimated and caused major problems to the client in the monetary planning. The purpose of this paper is to determine the most influential factors on the deviation of pre-tender cost estimation in Malaysia by conducting a survey. Design/methodology/approach: Fuzzy logic, combined with artificial neural network method (fuzzy neural network) was then used to develop an estimating model to aid the pre-tender estimation process. Findings: The results showed that the model is able to shift the cost estimation toward accuracy. This model can be used to improve the pre-tender estimation accuracy, enabling the client to take the necessary early measures in preparing the funding for a building project in Malaysia. Originality/value: To the authors’ knowledge, this is the first study on tender price estimation standardization for a construction project in Malaysia. In addition, the authors have used factors from literature for the model, which shows the thoroughness of the developed model. Thus, the findings and the model developed in this study should be able to assist contractors in coming out with a more accurate tender price estimation

Water demand management: a review on the mechanisms to reduce water demand and consumption

Securing water supplies in urban areas is a major challenge for policy makers, both now and into the future. In mitigating threats of a water shortage, a number of initiatives and programs have been implemented, which includes water demand management (WDM). A number of studies have analyzed the usage of various mechanisms to manage water demand. In this paper, we review the implementation and the effectiveness of the mechanisms of price, technology, communication /education and restriction in reducing domestic water demand. Based on the review, we have found that the effectiveness of the mechanisms varies from one mechanism to another, where rainwater harvesting system was found to yield the greatest water demand reduction, while communication/education yields the lowest. Despite the different approach, most of the cities reviewed used integrated implementation of the mechanisms to reduce water demand, which shows that the mechanisms need to be combined in order to maximise water demand reduction. However, currently there are still very limited studies conducted on the effective implementation of integrated mechanisms. Thus, more work is needed in order to strategize the usage of these mechanisms in maximising water demand reduction. It is expected that this study can assist water authorities in designing and conducting an effective WDM program in order to maximise water demand reduction

Project management practice and its effects on project success in Malaysian construction industry

The rapid economic development has increased the demand for construction of infrastructure and facilities globally. Sustainable development and globalization are the new 'Zeitgeist' of the 21st century. In order to implement these projects successfully and to meet the functional aim of the projects within their lifetime, an efficient project management practice is needed. The aim of this study is to identify the critical success factors (CSFs) and the extent of use of project management practice which affects project success, especially during the implementation stage. Data were obtained from self-administered questionnaires with 232 respondents. A mixed method of data collection was adopted using semi-structured interview and questionnaire approach. The result of the analysis of data obtained showed that new and emerging criteria such as customer satisfaction, competency of the project team, and performance of subcontractors/suppliers are becoming measures of success in addition to the classic iron triangle's view of time, cost and quality. An insight on the extent of use of different project management practice in the industry was also achieved from the study

Exploring quality dimensions from a construction perspective: a literature review

The quality of products and services is fundamental to organizational performance and reputation. A construction project entails meeting the specification criteria and standards of quality, finishing the task on time, and within the specified budget. Construction projects have different quality dimensions, and each can be measured from a different perspective. An exploratory research approach was used to explore the eight quality dimensions within the construction industry's perspective by exploring the quality issues within the Malaysian construction industry. The findings indicate that a related quality dimension is conformance or the degree to which a product's design and operating characteristics meet established standards. Thus, the study's significant contribution is the exploration of the eight quality dimensions from the construction industry's perspective. Thus, it is essential to ensure that the project meets the users' needs, and the best way to do this is to involve the users in the quality planning process. This will help ensure the project is designed and built to meet their needs and expectations. In conclusion, considering all the quality dimensions when planning and executing a construction project is essential, and by prioritizing these quality dimensions, we can ensure that the buildings are built with quality

Assessment on the performance of a rainwater harvesting system

The rainwater harvesting system is an alternative way to meet domestic water demand. At the same time, it can also help in reducing run-off, especially in urban areas. In this study, a rainwater harvesting system, which located at the Faculty of Engineering, was taken as a case study. Indicators that measure the performance of the rainwater harvesting system have been developed. One such indicators are reliability, which is dependent on the rainfall and water consumption patterns, tank size and effective roof area. Flow meter and rain gauge used to measure the volume of harvested rainwater and collect the rainfall depth data respectively. In this study also, a model is developed to predict the volume of rainwater harvesting with respect to the rainfall depth with a particular roof catchment. It demonstrates good fits with R2 = 0.952. The reliability of rainwater harvesting using existing tank 4.08 m3 is 60.8%, 66.5%, 67.7% and 98.2% for Consumption 1 (flushing toilets, gardening and washing vehicle), Consumption 2 (flushing toilets and gardening), Consumption 3 (gardening and washing vehicle) and Consumption 4 (flushing toilets and washing vehicle) respectively. The run-off coefficient for the selected roof is found to be 0.92

Applications of building information model (BIM) in Malaysian construction industry

Since the introduction of BIM in Malaysia in 2009, the technology adoption rate is slow when compared to other countries of the world. Most of the construction companies in Malaysia have an insight on the BIM concept but are yet to implement it in the management of their construction projects. By the year 2020, the Malaysian government will make BIM mandatory, this makes it important to carry out research on the possible applications of the technology. A qualitative method of enquiry was used for this study in Klang Valley using semistructured interview. The responses received were analysed using Principal component analysis (PCA). The result of the analysis showed that "quantity take-off and estimation", "clash detection and coordination", "integration and collaboration of stakeholders", and "design and visualisation" as the main applications of BIM in Malaysia presently. The implication of this findings is that the Malaysian construction industry productivity is likely to increase to meet the demand of the population through the implementations of BIM. More also, BIM technology is regarded as the future of construction industry, which makes it very important for the industry

Leadership in construction: a scientometric review

Leadership plays an increasingly important role in construction projects, and numerous research studies have been conducted. This study aims to identify the structure evolution development trends of this knowledge domain using visualisation analysis with CiteSpace. A total of 1789 peer-reviewed articles are collected from Scopus and the WoS core collection database to conduct a scientometric analysis. The results indicate that the US dominates this field and that institutions from Australia have made greater contributions. However, international cooperation in this area is not active. A total of eight co-citation clusters were identified, and the research of leadership in construction primarily focused on the topics of transactional leadership, safety leadership, team performance, leadership interaction processes and actual leader behaviour. The keywords co-occurrence evolution analysis was also conducted to provide a holistic knowledge map. Based on the development of this field and its current status, we propose trends and innovative research areas for future research. The findings in this research would help scholars to understand the structure and future trends of this field. Meanwhile, the research results would provide a reference for construction enterprises to formulate project manager competency criteria

Leadership in Construction: A Scientometric Review

Leadership plays an increasingly important role in construction projects, and numerous research studies have been conducted. This study aims to identify the structure evolution development trends of this knowledge domain using visualisation analysis with CiteSpace. A total of 1789 peer-reviewed articles are collected from Scopus and the WoS core collection database to conduct a scientometric analysis. The results indicate that the US dominates this field and that institutions from Australia have made greater contributions. However, international cooperation in this area is not active. A total of eight co-citation clusters were identified, and the research of leadership in construction primarily focused on the topics of transactional leadership, safety leadership, team performance, leadership interaction processes and actual leader behaviour. The keywords co-occurrence evolution analysis was also conducted to provide a holistic knowledge map. Based on the development of this field and its current status, we propose trends and innovative research areas for future research. The findings in this research would help scholars to understand the structure and future trends of this field. Meanwhile, the research results would provide a reference for construction enterprises to formulate project manager competency criteria

A bibliometric review on safety risk assessment of construction based on CiteSpace software and WoS database

As urbanization continues to grow around the world, the risks associated with construction are increasing. Scientific and practical risk assessments help reduce safety risks and achieve healthy, long-term growth, so there has been much research in this field. Through a review of the literature, this study aims to reveal the state and trends of research in the field of safety risk assessment. We searched 473 articles on construction risk assessment from the Web of Science (WoS) in the last decade, bibliometrically analyzed them, and then uncovered their significance using CiteSpace software (6.1. R6 (64-bit) Basic). The primary topics of conversation are countries, institutions, authors, and keywords, followed by references. According to the co-authorship analysis, the current research in this field is mainly from China, the USA, and Australia. Most influential authors currently have teaching or research positions at educational institutions; the most notable of which include Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Tsinghua University. They form a relatively close network of institutional cooperation. Based on the results of the co-term analysis, this study found that the current research hotspots are mainly focusing on “multi-objective optimization”, “risk management”, “mechanical characterization”, “mental fatigue”, “accident prevention”, and many others. Data-driven, AI-assisted, and multi-stakeholder participation are the future trends in this field

A Joint Bayesian Optimization for the Classification of Fine Spatial Resolution Remotely Sensed Imagery Using Object-Based Convolutional Neural Networks

In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban settings. As a result, combining deep learning and object-based image analysis (OBIA) has become a new avenue in remote sensing studies. This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. The proposed classification model achieved the best accuracy, with 0.96 OA, 0.95 Kappa, and 0.96 mIoU in the training area and 0.97 OA, 0.96 Kappa, and 0.97 mIoU in the test area, outperforming several benchmark methods including Patch CNN, Center OCNN, Random OCNN, and Decision Fusion. The analysis of CNN variants within the proposed classification workflow showed that the HybridSN model achieved the best results compared to 2D and 3D CNNs. The 3D CNN layers and combining 3D and 2D CNN layers (HybridSN) yielded slightly better accuracies than the 2D CNN layers regarding geometric fidelity, object boundary extraction, and separation of adjacent objects. The Bayesian optimization could find comparable optimal MRS parameters for the training and test areas, with excellent quality measured by AFI (0.046, −0.037) and QR (0.945, 0.932). In the proposed model, higher accuracies could be obtained with larger patch sizes (e.g., 9 × 9 compared to 3 × 3). Moreover, the proposed model is computationally efficient, with the longest training being fewer than 25 s considering all the subprocesses and a single training epoch. As a result, the proposed model can be used for urban and environmental applications that rely on VHR satellite images and require information about land use