Pavement Materials Testing Laboratories (PMTLs) play a critical role in ensuring that pavement materials meet

the required specifications to sustain traffic loads and achieve design standards. This study investigates the

potential for transforming PMTL operations through the adoption of automation technologies, with a particular

focus on developing-country contexts such as Papua New Guinea (PNG). A systematic review of the literature

published up to 2025 was conducted using targeted keyword-search strategies to identify relevant advances in

laboratory automation across multiple sectors. The review highlights significant progress driven by the

integration of artificial intelligence (AI), the Internet of Things (IoT), robotics, and Laboratory Information

Management Systems (LIMS). However, findings indicate that PMTLs remain largely dependent on manual

processes, limiting efficiency, traceability, and data reliability. To complement the literature review, a case study

capacity, remain significant barriers to implementation. This research contributes to the development of a

conceptual foundation for PMTL automation, highlighting the need for scalable, context-specific solutions to

enhance quality assurance and operational performance.

Keywords: Laboratory Automation, LIMS, PMT, Laboratory Quality Assurance, ISO/IEC 17025:2017

The performance and durability of road infrastructure depend on the quality and compliance of the construction

materials. PMTLs play a critical role in verifying that these materials meet the required engineering

specifications and performance standards [17]. Reliable and timely laboratory testing is therefore essential to

ensure that pavement systems can withstand increasing traffic demands and achieve design expectations

[13],[17]. However, in many developing countries, including PNG, PMTL operations remain largely manual,

have significantly improved laboratory operations in other sectors [11],[12]. These technologies enable

automated data capture, real-time monitoring, and integrated workflows, leading to improved accuracy,

consistency, and efficiency [1]. Despite these benefits, their adoption in PMTLs remains limited, particularly in

resource-constrained environments. This limited adoption is largely due to constraints such as reliance on legacy

systems, inadequate infrastructure, financial limitations, and a lack of technical expertise. In addition, concerns

regarding long-term sustainability, including system maintenance and workforce capacity, continue to hinder

implementation. Consequently, many PMTLs are unable to fully benefit from digital transformation, and

inefficiencies persist in key operational processes. Although automation has been widely studied in other

laboratory domains, there is limited research on its integrated application within PMTLs. In particular, there is

a lack of end-to-end systems capable of linking all laboratory functions within a unified framework. Addressing

outcomes. Ultimately, this contributes to stronger quality assurance and more durable road infrastructure systems.

The development of a user-friendly, scalable, and cost-effective automated laboratory system, particularly in

resource-constrained environments, requires a clear understanding of existing technologies and their practical

use. This review examines current advances in laboratory automation and evaluates their relevance to PMTLs.

The focus is on key elements such as system integration, data management, and process optimisation, with

consideration of compliance requirements under ISO/IEC 17025:2017 [7],[9]. The review also identifies

common benefits and implementation challenges. This provides a basis for selecting appropriate technologies

that suit the operational and financial constraints of PMTLs.

and trends in large datasets. These tools support faster decision-making and more reliable interpretation of results

[6]. Robotic Process Automation (RPA) is used to handle repetitive and rule-based tasks such as data entry,

sample logging, and report generation. This reduces the manual workload and improves consistency [10],[12].

Information technology (IT)-based optimization systems improve workflow efficiency and reduce turnaround

time. These systems are particularly useful in high-demand laboratory environments [15],[2]. Cloud computing

provides flexible and secure platforms for storing and processing laboratory data. It also supports system

integration and allows laboratories to scale their operations as needed [2],[5]. Laboratory Information

Management Systems (LIMS) act as the central platform for managing laboratory activities. LIMS supports

sample tracking, workflow control, equipment monitoring, data analysis, and reporting, while also improving

compliance with regulatory standards [1],[8].

reliable. Automation also reduces dependence on manual oversight and strengthens overall process control. In

road construction and engineering applications, improved QA supports better decision-making and contributes

to higher-quality road infrastructure outcomes.

Automation supports compliance with ISO/IEC 17025:2017 by improving control, consistency, and traceability.

across laboratory operations. In document control, automated systems ensure that only current and approved

versions are used. Version control and approval workflows reduce the risk of outdated documents. Equipment

management is also improved. Systems can schedule calibration, track maintenance, and issue alerts when

equipment is due for servicing. This helps prevent the use of non-compliant equipment. Automation enhances

data integrity by enabling secure, paperless data handling. It reduces transcription errors and protects data from

Automation improves laboratory performance in several ways. It enhances traceability by providing real-time

access to data. It also reduces human error by limiting manual data handling. Workflows become more efficient

and consistent through standardization. This leads to improved data quality and reproducibility of results.

Automation can reduce operational costs by improving resource utilisation and minimizing waste. It also

shortens turnaround time, as systems can operate continuously and perform multiple tasks simultaneously. In

addition, automation improves safety by reducing human exposure to hazardous environments and enabling

better monitoring of laboratory conditions.

Despite its advantages, automation presents several challenges, especially in developing or resource-limited

resources. This highlights the need for practical, scalable, and context-specific automation strategies.

This study adopts a systematic review and framework development approach to investigate and establish an

automation model for PMTL processes. The methodology integrates a structured literature analysis with

conceptual system design to identify, evaluate, and synthesise appropriate automation technologies applicable

to laboratory operations. The approach is guided by the objective of developing a practical, scalable, and cost-

effective automation framework tailored to resource-constrained environments. Emphasis is placed on

enhancing laboratory efficiency, improving data integrity, and ensuring compliance with ISO/IEC 17025:2017

requirements. The methodology further incorporates an applied component through a case-based assessment to

including request submission and billing, sample receipt and registration, test allocation and scheduling, data

recording and processing, and result validation and reporting. The assessment revealed that PMTL operations

remain predominantly reliant on manual and paper-based processes. This reliance contributes to fragmented

workflows, limited integration between laboratory functions, and inefficiencies in data management and process

coordination. Based on these observations, a gap analysis was undertaken to evaluate the discrepancies between

integrated PMTL automation framework. Furthermore, the identified gaps informed the selection of appropriate

strengthening overall laboratory performance.

Figure 1:: PMTL Manual Process

Based on the outcomes of the needs assessment and gap analysis, a conceptual PMTL automation system

architecture was developed to address identified inefficiencies and operational limitations. The system design

adopts a cost-effective, customized, modular, and integrated approach, enabling interoperability between

laboratory processes, equipment, and data management platforms. The proposed architecture consists of the

following customized core components: LIMS serves as the central platform for managing sample registration,

workflow coordination, data processing, and reporting. IoT Integration enables real-time monitoring of

architecture ensures real-time data flow, improved traceability, and enhanced compliance with ISO/IEC

17025:2017 requirements.

Table 1: Case Study Response Summary

validation was also undertaken using data collected from PMTL personnel and contractor representatives. The

responses were analysed to evaluate the level of acceptance, perceived usefulness, and readiness for adopting

the proposed system. The findings indicated strong support for automation and confirmed the relevance of the

framework in addressing current operational challenges within the PNG context. The integration of these

validation methods ensures that the proposed framework is not only theoretically robust but also practically

viable. It demonstrates strong alignment with user needs, industry practices, and operational realities, making it