Construction Contracts Research Articles

Automated Risk Analysis for Construction Contracts Using Neural Networks

Automated Risk Analysis for Construction Contracts Using Neural Networks

Joint and several liability at the interface of national and international law and the FIDIC conditions of contract

Abstract The international terms of the 2017 FIDIC Red Book (reprinted and amended in 2022) state that, when the contractor is a joint venture (JV), all members are jointly and severally liable to the employer. These terms also establish certain procedural rules—the most important ones being the submission to the employer of the JV undertaking and the appointment of a leader. However, these international terms do not provide for substantive rules pertaining to the plurality of the JV members. These require reference to the applicable law that governs the construction contract. The applicable law can be domestic law, such as Qatari law, or an international soft law, such as the Unidroit Principles. Comparing these two possibly applicable laws, it becomes evident that there are no significant differences between the two regarding the substantive rules concerning a plurality of obligors. Furthermore, it is argued that the majority of these substantive legal rules, whether national or international, are inoperative in a construction contract incorporating the international terms of the FIDIC Red Book.

Application of large language models to intelligently analyze long construction contract texts

The emergence of large language models (LLMs) has provided an opportunity to assist humans in quickly reading, searching, and understanding the contents of construction contracts. However, the limited context length of LLMs restricts their ability to process lengthy contract documents, which hinders their application in the construction industry. This study proposes intelligent analysis methods for long construction contracts, which enables LLMs to handle contracts that exceed their context length through a two-stage text segmentation. We further utilized the segmented text chunks for content compression and intelligent question-answering applications. The FIDIC contract was used for testing. A condensed version of lengthy contracts summarizes the provisions in a shorter format. It maintains a high level of correctness and readability, offering practitioners additional options to read contracts of varying lengths based on their needs. The satisfaction rate of the question-answering outcomes reached 93.3%, allowing practitioners to quickly obtain specific clauses of interest and relevant contract knowledge through personalized queries. The performance of the 8K model using our methods is comparable to that of the 128K long-text models, while reducing computational power. This study expands the potential for applications of LLMs in contract management within the construction industry.

A construction contract administration (CCA) framework for graduate architects’ professional development

PurposeConstruction contract administration should be carried out by a person (normally engineer or architect depending on the type of contract) with expert technical knowledge of the construction process, strong leadership quality, highly developed interpersonal skills and an understanding of contractual, legislative and statutory underpinnings. However, due to a shortage of architects, graduate architects have been assigned to perform a partial role. Architectural firms are disappointed with the quality of the graduates and still note that they have to mentor them to be competent for their jobs. This paper aims to develop a construction contract administration (CCA) framework for graduate architects that aids the smooth running of a construction contract.Design/methodology/approachSeven housing projects in Malaysia have been selected as case studies. Documentation review and semi-structured interviews relevant to selected case studies were conducted to collect data to develop the CCA framework. The draft framework will then be validated through a focus group study.FindingsA total of 5 themes and 11 sub-themes with core tasks were developed in the CCA framework. The themes involved are claims and legal matters management, communication and relationship management, project management, design management and quality assessment and management. Sub-themes are authority matters, building certification, meetings, coordination checklist, letter writing, contract documentation, building material, design brief, building sustainability and workmanship quality standards, contractors’ submission and building details.Originality/valueThe contribution of this study can be utilized for developing further models in the future that would improve the work performance of graduate architects. The framework assists the university by moving forward to the professional practice stage. In addition, it indirectly enhances the potential of graduate architects to perform efficiently and intensifies the bonding between project stakeholders.

Evaluating the project management maturity in the construction industry of developing countries, Ghana in perspective

This study evaluates construction contractors' project management maturity levels, examining various factors such as ISO certification, capacity building programs, contractor type (road vs. building), and specific knowledge areas. Data was gathered through surveys and assessments to evaluate the maturity of different processes and practices. The findings reveal various maturity levels across the industry, with significant variations among contractors. Common challenges include gaps in risk management, safety protocols, and overall project management practices. Factors such as ISO certification and capacity-building programs positively impacted maturity levels. However, targeted training and education are crucial for addressing specific knowledge gaps. The analysis identifies critical recommendations for improving project management maturity in the construction industry, including fostering a culture of continuous improvement, promoting knowledge sharing, implementing tailored training programs, and developing industry-specific standards. By addressing these challenges and implementing appropriate strategies, the construction industry can enhance project outcomes, reduce costs, and improve competitiveness.

Intelligent identification of risks in construction contract clauses based on semantic reasoning

Purpose The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.Design/methodology/approach This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.Findings The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.Originality/value This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.

Extension of Time and Liquidated Damage Issue in Construction Contract: Nepalese Legal Provision and Practice

Extension of Time and Liquidated Damage Issue in Construction Contract: Nepalese Legal Provision and Practice

Adoption of Life Cycle Thinking: Impact-driven comparative assessment of Japanese construction corporations’ trends in practices

Although life cycle thinking offers a well-founded set of concepts, methods, and tools for organizational contexts, only little is known about its relevance in the construction sector. In this sector, construction corporations are key players. In this work, a novel impact-driven method to assess the adoption of life cycle thinking by construction corporations is introduced. Japan, as a high-income country with a relatively long history of environmental policies informed by material flow analysis, is chosen as the context. Five Japanese construction corporations and respective reporting are used as case studies. Trends in environmental impacts over time of the case study corporations are assessed using two indicators: resource and carbon productivity. In comparison, an automated text corpus analysis workflow is presented to explore the corporate report’s life cycle thinking-related content in a meaningful way. Comparing the period of 2014–2018 and 2019–2023, an overall relative increase in framework adoptions (77%) and their integration in corporate categories (85%) in corporate reports indicates its increased procedural relevance among Japanese construction contractors. Findings show how the carbon indicator is embedded in various frameworks and, for instance, reveal an increased relevance of Scope 3 emissions as a framework on a low level. However, Scope 3 is also utilized as a performance indicator. To this end, considerable temporal differences in adoption practices are observed. Finally, a potentially effective corporate adoption model is identified through the comparative research design. The proposed method can be applied to other construction corporations and regional contexts.

Project-oriented strategic management of UK construction firms – towards corporate diversification: critical literature review

PurposeThe paper analyses the strategic management strategies of UK Construction Contracting Firms (CCFs) and their impact on the industry, highlighting their fragmentation, high-risk, low-profit nature and low profit returns. It suggests proactive strategies for sustainable growth and explores the potential of corporate diversification.Design/methodology/approachUtilising a thematic critical literature review, specific inclusion/exclusion criteria are used to select relevant literature together with a thematic qualitative synthesis data analysis approach to identify trends and challenges.FindingsUK CCFs primarily use reactive and project-based strategic management, which may not align with long-term success due to market volatility, reactive supply, fragmentation, high competition and lack of differentiation. The short-term business cycle makes medium- to long-term strategy development difficult. It is recommended that CCFs adopt proactive strategic management and consider corporate diversification for enhanced competitiveness, stability and performance. Although there are conflicting findings on the impact of diversification on firm performance, this research suggests that it is a viable strategy for achieving enhanced firm performance and competitive advantage.Research limitations/implicationsThe importance of proactive corporate strategies for CCFs is emphasised to overcome industry challenges, promote sustainable growth and enhance competitiveness. Corporate diversification, cultural transformation, management qualifications promotion and talent development collaboration are advocated, providing valuable insights for practitioners, policymakers and researchers.Practical implicationsThe practical implications of this research involve fostering a shift towards proactive and dynamic strategic management in the UK construction industry, addressing the historical neglect of broader strategic perspectives and empowering practitioners and academics to drive positive change and innovation within the sector.Social implicationsThe social implications of this research encompass the potential to enhance the overall efficiency, sustainability and collaborative dynamics within the UK construction industry, which can ultimately contribute to improved infrastructure development and the well-being of communities.Originality/valueProject-driven strategic management in the UK construction industry is explored, questioning the reactive approach used by CCFs. It provides insights, best practices and improvement areas, emphasising diversification, proactive corporate strategies, cultural shifts and industry improvement, spanning theory, practice and theory.

Multi-objective optimal allocation of construction project risks, ant colony optimization algorithm

PurposeThe purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.Design/methodology/approachBy defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.FindingsThe proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.Research limitations/implicationsThe proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).Practical implicationsBy implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).Originality/valueIn this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

A Study on the Distinction between Construction Project Owners and Construction Contractor under the Occupational Safety and Health Act - Focusing on Lower Court Cases -

A Study on the Distinction between Construction Project Owners and Construction Contractor under the Occupational Safety and Health Act - Focusing on Lower Court Cases -

Exploring Bid/No-Bid Decision Factors of Construction Contractors for Building and Infrastructure Projects

While contractors may experience financial failure if they bid on an inappropriate project, bidding on the right project may allow them to profit substantially. Therefore, understanding the various factors that influence the bid/no-bid decision is crucial for construction companies in determining whether to pursue a project. The present study aims to identify the critical factors influencing contractors’ bid/no-bid decisions. A total of 112 responses were collected from a questionnaire survey to rate the relative importance of 22 factors, and these factors were then analyzed based on the type of project and the contractor’s years of experience. The results indicate that the client’s ability to pay, clarity of the scope of work, project cash flow, the need for work, and availability of qualified labor are the most critical factors influencing contractors when making bid/no-bid decisions. The factor “previous experience in similar projects” was statistically significant among building and infrastructure projects, while “project location” was statistically significant among contractors with different years of experience. Finally, factor analysis identifies the six major underlying groups: client-related factors, bidding-related factors, contractor-related factors, market-related factors, economy-related factors, and project-related factors. The study’s findings can help contractors better understand the factors influencing their bidding-related decisions.

Whether Behavioral Guidance Policies of Construction Waste Resource Utilization Are Effective for Construction Contractors: Evidence from China

Whether Behavioral Guidance Policies of Construction Waste Resource Utilization Are Effective for Construction Contractors: Evidence from China

Study on Factors Affecting Performance in the Construction Business of Contractors in the Pakse Municipality, Champasack Province, Lao PDR Case study: Building construction business

The purpose of this research is to examine the key factors that affect the construction business of the construction companies in the Pakse Municipality, Champasack Province of the Lao People's Democratic Republic, which include the business model and operations of the construction company, as well as the analyze of various factors that affect the success of the business. Additionally, the research investigates the causes and methods for preventing or resolving such issues, considering the project's two critical aspects. The data were obtained from 53 construction company utilizing questionnaires. The data was further statistically examined to determine the factors' relative relevance and test the hypothesis. Research results showed 12 major problems: 1) project owner does not pay the agreed-upon construction costs; 2) lack of pre-employment financial assessment; 3) lack of personnel planning; 4) lack of motivation to work; 5) inflation and country's economic insolvency; 6) registered capital and performance of the company; 7) overproduction and overpricing; 8) lack of mechanical planning; 9) lack of planning and control of material use; 10) competitors who are companies from other provinces or abroad; 11) shortage of subcontractors and skilled labor; 12) encouragement-promotion and knowledge of modern construction techniques from the government. Altogether, the two fundamental factors indicated that the most significant issue is within the project, specifically the finances and building budget, with an average mean score of 4.18, which is 100%. As for personnel and staff, the average mean score of 4.14 covers 100%. In terms of sourcing, the average mean score of 3.85, accounting for 83.33%; for tools and construction machinery, the average mean score of 3.78, representing 83.33%. Considering building materials, the average mean score of 3.74, forming 71.42%, and in terms of environment and regulations, the average mean score of 3.74, equivalent to 60 percent of the problems with an average mean score greater than 3.5. The hypothesis testing showed that the value is less than the value. In summary, factors that affect the operation of construction contractor businesses do not depend on whether the contractor is profitable or at a loss/equity, or the importance of the factors does not depend on performance.

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner.

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

CLASS AT THE CROSSROADS: Reframing Disadvantage in Organizing Daily Wage Work in Western India

AbstractAcross Indian cities, daily wagers gather every morning at large intersections or crossroads (nakas) where they seek work for the day from small construction contractors. In the satellite city of Navi Mumbai (New Mumbai), some of these daily wagers are reconstituting themselves into a class of ‘disadvantaged, crossroad workers’. This article provides an ethnographic narration of how class is experienced, constituted and asserted at a street crossroad. Through the space of the naka, daily wagers combine their different experiences of caste, religious and regional disadvantage into a collective identity of crossroad workers. As a collective, they seek and gain recognition as workers by the state, even if their everyday terms of work continue to be largely unprotected by law. While such reframing of disadvantage has long been part of social movements in western India, their contemporary politics is conditioned by workers’ alienation from new town‐making projects, where they are seen as temporary labor migrants and must contend with landed, socio‐politically dominant groups vying for control over the city. This article contributes to growing scholarship on the resocialization of labour movements, as both work and class organizing change dramatically, particularly in contentious urban spaces like Navi Mumbai.

Modeling and significance assessment of road construction participant and user benefits using expert evaluation methods

Human activities are related to obtain economic, technical, social and environmental benefits. The road construction process participants and road users have direct or indirect benefits from elaboration of new infrastructure. The number of the benefits received by individual entities is publicly discussed by politicians, lobbyists, experts and other decision-makers without having quantitative estimates of benefits, i.e., often relying on intuition or considering theoretical reasoning. The paper suggests a system of 19 benefit entities (criteria) assigned the ranks given by experts. The study involved three categories of experts, including 35 road engineers, 36 transport engineers and 61 road users. The values of the concordance coefficients obtained as a result of the conducted research were found significantly higher than critical values and showed that the opinions of the experts in each category were consistent (not contradictory). This made it possible to consider the average of the opinions of the expert group as a reliable result of solving the problem. Rank averages were replaced by the normalized weights of criteria using Average Rank Transformation into Weight-Linear (ARTIW-L) and – Nonlinear (ARTIW-N) methods. The global averages of criterion weights were used employing the Inverse Hierarchy for Assessment Main Criteria Importance (IHAMCI) method thus calculating the normalized weights of the road-related classified entities (three main criteria). The findings prove that road users benefit the most (weight 0.3485), the road construction contractor (weight 0.3325) is in the next position and the road owner (investor) takes the weight equal to 0.3190. The generated research data can be used for justifying the rationality of road investment.

Актуальные особенности договорной формы долевого строительства объектов коммерческой недвижимости

The author has substantiated the actual aspects of shared-equity construction contracts for commercial real estate. Contractual and corporate forms of implementation of shared-equity commercial construction relations are united by common goals, risks, conditions of joint investment and construction mandatory design and safety requirements. The main participants in the contractual (non-corporate) form of shared-equity commercial construction within the framework of investment projects can be individuals with the status of sole proprietors and corporate entities — investors or contractors. The implementation of shared-equity commercial construction relations is possible with the use of named (simple partnership agreement, investment agreement, shared-equity construction agreement, etc.), as well as unnamed contracts. In order to establish the commercial nature of the shared-equity construction contract, it is advisable to supplement the norms of the relevant legislation with expanded entrepreneurial powers of its parties.

Forecasting the outcomes of construction contract disputes using machine learning techniques

PurposeThis study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.Design/methodology/approachThis study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.FindingsThe analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.Originality/valueThis study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

Data Monetization Strategies: The Construction Industry Market Analysis

Data monetization is leveraging data to obtain economic benefits. In the context of Big Data Analytics (BDA), data serves as a fundamental asset, enabling the transformation into valuable insights throughout various construction phases. Unfortunately, there is lack of studies focusing on data monetization within the construction industry, given the unique supply and demand characteristics among construction stakeholders. This study aims to address the objective of identifying data monetization strategies applicable to the construction industry, particularly for data sellers and data buyers. Data was gathered through a quantitative survey among 100 construction practitioners in Malaysia, encompassing developers, contractors, consultants, government agencies, technology providers, and academia. Respondents were further categorized into data sellers and data buyers. The data were analyzed using mean analysis, t-test and Spearman's rank correlation coefficient. The study identified six data monetization strategies, comprising 19 determinants. The analysis revealed high preference on all data monetization strategies and moderate differences in the preferred strategies between data sellers and buyers. Significant differences were found in 3 determinants which are (1) trading data to facilitate decision-making, (2) trading data for construction reports, benchmarks, and indices, and (3) selling visualized data on real-time platforms. The t-test indicated that data sellers are more inclined towards the three strategies for effective monetization. Furthermore, Spearman’s correlation coefficient revealed the 3 determinants also positively influence another 3 determinants of (1) data wrapping to reflect better service from data provider, (2) buying raw data with its inherit information and (3) monetizing internal data to optimize organization’s work process. The insights enable stakeholders to implement mechanisms that foster data monetization within project cultures accelerating BDA undertakings. Future recommendations include using larger sample sizes to enhance generalizability and to explore more areas such as construction contracts, cost, health, and safety.